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Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science

Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science The Vera C. Rubin Observatory is expected to start the Legacy Survey of Space and Time (LSST) in early to mid-2025. This multiband wide-field synoptic survey will transform our view of the solar system, with the discovery and monitoring of over five million small bodies. The final survey strategy chosen for LSST has direct implications on the discoverability and characterization of solar system minor planets and passing interstellar objects. Creating an inventory of the solar system is one of the four main LSST science drivers. The LSST observing cadence is a complex optimization problem that must balance the priorities and needs of all the key LSST science areas. To design the best LSST survey strategy, a series of operation simulations using the Rubin Observatory scheduler have been generated to explore the various options for tuning observing parameters and prioritizations. We explore the impact of the various simulated LSST observing strategies on studying the solar system’s small body reservoirs. We examine what are the best observing scenarios and review what are the important considerations for maximizing LSST solar system science. In general, most of the LSST cadence simulations produce ±5% or less variations in our chosen key metrics, but a subset of the simulations significantly hinder science returns with much larger losses in the discovery and light-curve metrics. NASA Postdoctoral Program Fellow. LSSTC Catalyst Fellow. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. 1 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Unified Astronomy Thesaurus concepts: Trans-Neptunian objects (1705); Asteroids (72); Small Solar System bodies (1469); Near-Earth objects (1092); Comets (280); Interstellar objects (52); Sky surveys (1464); Main belt asteroids (2036) Supporting material: animations, machine-readable table 1. Introduction time is expected to be used for non-WFD observing and will likely be split between observing other portions of the sky in The Vera C. Rubin Observatory is currently under minisurveys (taking up more than a few percent of the construction on Cerro Pachón in Chile. When completed, the observing time) with different cadences, microsurveys (obser- observatory will house the 8.36 m Simonyi Survey Telescope ving strategies that require ∼1% of the observing time), and equipped with the Rubin Observatory LSST Camera 2 approximately 5% of the on-sky time dedicated to Deep (LSSTCam), which covers a 9.6 deg circular field of view Drilling Fields (DDFs; a small number of dedicated pointings (FOV). This provides the unique depth and temporal sky that will receive intensive observing at a higher cadence than coverage that will enable Rubin Observatory’s planned 10 yr the WFD). For a full description of the various components of Legacy Survey of Space and Time (LSST; Ivezić et al. 2019; the LSST and the requirements set by the SRD, readers are Bianco et al. 2022) to be an unprecedented discovery machine directed to Ivezić & the LSST Science Collaboration (2013), for solar system small bodies. With survey operations currently Ivezić et al. (2019), Bianco et al. (2022), and references therein. expected to begin in early to mid-2025, current predictions How exactly Rubin Observatory will scan the night sky is estimate that Rubin Observatory will detect over five million not fully settled. The Rubin Observatory Project and Opera- new solar system objects. In each of the solar system’s small tions teams have engaged with the wider user community to body reservoirs, an order of magnitude more objects will be optimize the LSST observing strategy in order to maximize the discovered during the LSST than cataloged to date in the Minor 37 future science returns from the resulting data set and facilitate Planet Center (MPC; Jones et al. 2009, 2018; LSST Science the best science with the survey (Bianco et al. 2022). Collaboration et al. 2009; Solontoi et al. 2010; Shannon et al. Partitioning out the non-WFD LSST observing time and fine- 2015; Grav et al. 2016; Silsbee & Tremaine 2016; Vereš & tuning the WFD observing cadence can be likened to cutting a Chesley 2017; Schwamb et al. 2018a; Ivezić et al. 2019; cake and dividing it out to attendees at a birthday party. There Fedorets et al. 2020a). In addition, the survey is expected to are many ways to cut and serve the slices of cake, but the discover at least several interstellar objects (ISOs) passing various slicing/serving options may result in very different through the solar system (Moro-Martín et al. 2009; Cook et al. outcomes. For example, cutting even slices such that everyone 2016; Engelhardt et al. 2017; Trilling et al. 2017; Seligman & gets the same portion size of cake is much more equitable and Laughlin 2018; Levine et al. 2021; Hoover et al. 2022). Beyond will likely result in a much happier crowd than cutting half the discovery, the dawn of Rubin Observatory will also usher in a cake for the first person served and dividing the other half of revolution for time-domain planetary astronomy. The LSST the cake among the rest of the attendees. LSST has four key will monitor most of its five-million-plus small body science drivers: probing dark energy and dark matter, exploring discoveries over a 10 yr period, with likely hundreds of the transient optical sky, inventorying the solar system, and observations per object split across six broadband (ugrizy) mapping the Milky Way (LSST Science Collaboration et al. filters (LSST Science Collaboration et al. 2009; Ivezić et al. 2009; Ivezić et al. 2019). What may be beneficial for one 2019). This will enable an unparalleled probe of activity within science driver in a proposed LSST observing cadence may various regions of the solar system, including cometary negatively impact the returns from another. Optimizing the outgassing/sublimation, cometary outbursts, rotational breakup LSST strategy is thus a fine balance to tune the cadence events, and asteroid collisions (Jones et al. 2009; LSST Science parameters to obtain the best science from each of the LSST’s Collaboration et al. 2009; Schwamb et al. 2018a, 2021). The key drivers while evenly distributing the “unhappiness” such large number of observations per object will also provide that no science area is overly impacted by the finalized cadence opportunities to study rotational light curves, phase curves, and decisions. photometric colors that probe the shape, size, rotation rate, and As highlighted in Bianco et al. (2022), optimizing the LSST surface composition of these small bodies (Jones et al. 2009; cadence is a multivariate problem. In order to facilitate LSST Science Collaboration et al. 2009; Schwamb et al. exploring the various options for modifying the LSST survey 2018a). The LSST will be a collection of surveys operating in strategy and the resulting impacts on the survey’s main science tandem. The main component of the LSST is the Wide–Fast– drivers, the Rubin Observatory LSST Scheduler Team has Deep (WFD), a wide-field survey covering ∼18,000 deg of developed a suite of cadence simulations (Connolly et al. 2014; the sky with a universal observing strategy. Although there is Delgado et al. 2014; LSST Science Collaboration et al. 2017; tuning to the implementation of the WFD that is possible, the Jones et al. 2020) using the Rubin Observatory scheduler main requirements for the WFD are outlined in the LSST (rubin_sim/OpSim; Naghib et al. 2019) and the Python- Science Requirements Document (SRD; Ivezić & the LSST based LSST Metrics Analysis Framework (MAF; Jones et al. 2014). The Rubin Observatory Survey Cadence Optimization Science Collaboration 2013). The SRD defines the WFD as of sky uniformly covered to a median total of Committee (SCOC) has been synthesizing the feedback from ∼18,000 deg 825 ∼30 s exposures divided across the six filters over a 10 yr the LSST user community and the output from the MAF period. Approximately 80%–90% of the LSST’s on-sky metrics to produce a formal recommendation on how to observing time will be devoted to the WFD. The remaining optimize the LSST survey strategy (Ivezić & the SCOC 2021; Bianco & the SCOC 2022). The SCOC is expected to finish its https://www.minorplanetcenter.net/ main deliberations by the end of 2023. The SCOC may request 2 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. some additional fine-tuning of the observatory strategy and are currently not explored in the cadence simulations that have recommend changes for the first year of the survey based on the the potential for hindering or enhancing solar system science with the LSST. Finally, we draw together in Section 6 knowledge gained during commissioning and benchmarking of conclusions and recommendations for tuning the LSST survey the telescope−camera system (Bianco et al. 2022). Once Rubin strategy in order to maximize solar system science and identify Observatory science operations start, it is expected that the areas for future work. Given the length of this paper, we have SCOC will periodically review the performance of the LSST included a table of acronyms and their expansions in observing cadence and subsequently recommend modifications Appendix A in Table 7. as needed. This paper is a contribution to the Astrophysical Journal 2. Simulating LSST Solar System Detections Supplement Series focus issue on Rubin LSST Survey Strategy Optimization. The focus issue aims to capture the knowledge Simulating observations of solar system objects requires learned during the process of selecting and finalizing the LSST considerations beyond those commonly used for most other initial cadence and identifying what observing strategies are or astrophysical sources. Of foremost importance are their are not suitable for each of the key LSST science areas. We nonsidereal motions and the fact that a common rest frame refer the reader to the opening paper by Bianco et al. (2022) for cannot simultaneously approximate all of them. Solar system −1 a more detailed introduction to the focus issue. The work object proper motions range from 1″ hr for distant trans- −1 presented in this paper stems from the Rubin Observatory Neptunian objects (TNOs) to 1° hr for closely approaching LSST Solar System Science Collaboration’s (SSSC) efforts to and impacting near-Earth objects (NEOs). Next, their bright- provide feedback to the SCOC. The LSST SRD does not set nesses may greatly vary depending on their orbits around the performance requirements based on detecting a certain number Sun and how closely they approach Earth. Furthermore, of solar system objects in the various small body populations. cometary activity (i.e., sublimation-driven mass loss) can Instead, the SRD outlines the minimum requirements and enhance or even dominate the intrinsic brightness of active stretch goals for the observing specifications of the LSST such objects in response to solar insolation and make them extended as single exposure depth, sky coverage, number of visits, objects. Finally, their brightnesses also vary with the phase astrometric precision, and coadded 10 yr depths that would (Sun-target-observer) angle. Other brightness variations, e.g., enable science in all four main survey science drivers. What it due to the rotational light curve, or outbursts of activity, can be means to maximize the returns on LSST Solar System science treated in ways similar to any other astrophysical source. in the context of survey cadence decisions is up for To partially illustrate the added complexities of modeling interpretation by the Rubin data rights community and the solar system objects, take, for example, a 1 km radius object in SCOC. The baseline survey strategy that was being simulated a parabolic orbit observed at solar opposition. Such an object at the start of the cadence optimization process showed an would have an apparent magnitude of order-of-magnitude increase in solar system discoveries across mH=+ () 1, 1, 0 5 log(r)+ 5 log(D)+ 2.5 log(F), 10 10 10 each of the minor planet populations (LSST Science Colla- () 1 boration et al. 2009; Vereš & Chesley 2017; Jones et al. 2018). Determining that the LSST needs to discover N objects of class where H(1, 1, 0)(or, more simply, H) is the absolute X to measure Y at the Z confidence level in order to provide the magnitude. Parameter r is the heliocentric distance in next leap forward in our understanding of the solar system is h astronomical units, Δ is the observer-target distance in extremely challenging to do. Many of the science questions that the LSST will address are not necessarily well understood (or astronomical units, and Φ is the phase function evaluated at even formulated) yet. In most cases, it is very difficult to take phase angle f. Let the 1 km object have a geometric albedo existing models of the solar system, its formation and of 4%, and then H; 17.6 mag. The apparent brightness of this evolution, and transform that into the the total number of target would range from 25th magnitude at 6 au to 17th particular kinds of objects needed and the photometric magnitude at 1.5 au from the Sun, within Rubin Observatory’s precision required to distinguish between the available models. nominal capabilities. If the 1 km object was active, the coma The analysis presented in this work is the SSSC’s attempt contribution to small-aperture photometry may be estimated as based on the collaboration’s science priorities (Schwamb et al. 2018a) to find quantitative proxies that can be calculated within mH=+ 2.5() 2-k log(r)+ 2.5 log(D)+ 2.5 log(F), cy h c 10 10 10 MAF and use these outputted metrics to identify which () 2 potential LSST observing strategies are the best and worst at enabling solar system science. where H is the cometary absolute magnitude, k is the In this paper, we review the LSST cadence simulations and heliocentric distance power-law slope for activity, and Φ is MAF metrics focusing on the impact on the detection and the phase function of the coma. Here the apparent magnitude monitoring of solar system minor planets and ISOs. In varies as Δ, rather than Δ , in order to account for the spatial Section 2, we provide an overview of how LSST moving extendedness of the coma and fixed-angular photometric object discoveries are simulated and how the relevant MAF apertures where the aperture is smaller than the apparent size metrics are calculated. Section 3 briefly describes the LSST of the coma. Let k = −4, and the comet with m = 25 mag at cadence simulations utilized in this work. In Section 4,we 6 au may brighten to m = 13 mag at 1.5 au. Move our examine the impact of various survey strategy choices and identify tension points with moving object detection and Defined as the apparent magnitude of the target as seen by the Sun at a characterization. In Section 5, we discuss additional factors that distance of 1 au (i.e., r = 1 au, Δ = 1 au, f = 0°). Ratio of the brightness at 0° phase to that of a white disk with the same https://iopscience.iop.org/journal/0067-0049/page/rubin_cadence geometric cross section. 3 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. hypothetical 1 km object to an inner-Earth orbit, and the LSST into account their motion and expected changes in brightness. As a first step, ephemerides are generated from the sample may not even observe it if survey operations never allow for orbits using OpenOrb (Granvik et al. 2009); the precise low solar elongation (low-SE) observations. Thus, solar system camera footprint is applied to determine which detections could objects have the potential to be undetected at some epochs be acquired based on their positions. Then, trailing losses and during LSST operations, saturate during others, or be missed color terms between the reference band and the observed filter altogether. are added to each record, to be used later when combined with In order to address the above challenges when simulating a (potentially modified) H value to calculate apparent observations of individual objects, a survey simulator must magnitudes for each observation. have knowledge of a target’s orbit and its activity state (e.g., We use a typical sample size of 5000 orbits per population. cometary or inactive). Furthermore, to assess a survey’s ability This generally provides enough statistical accuracy across the to detect, discover, and characterize solar system object orbital distribution to reach accuracies of a few percent at the populations, distributions of representative orbits that account 50% completeness level for discovery and characterization for the variety of orbits are also needed. Model distributions of metrics, while keeping compute requirements for a few solar system small bodies’ orbits (and their physical properties) hundred simulations reasonable. We then clone the potential are desirable. Such models are generally derived from the observations of these orbits over a range of H values (a simple known solar system populations but debiased to account for linear array, chosen with appropriate values for each individual discovery efficiencies. The survey simulator and solar system population), in order to be able to measure discovery and object orbital distributions are described in Sections 2.1 and characterization metrics across the expected range of obser- 2.2, respectively. The metrics used to analyze the simulated vable values for each population. The cloning takes place as observations are described in Section 2.3. part of metric calculation, within the MAF module of rubin_sim. At the metric calculation stage, the measured 2.1. Rubin Observatory Scheduler and Operations Simulator apparent magnitude is generated for each observation provided by the movingObjects module, taking into account each Various aspects of the current and previous iterations of the individual H value within the range used for cloning, as well as Rubin Observatory scheduler and operations simulator the effects of phase angle, distance from Earth and the Sun, (OpSim) are described in Connolly et al. (2014), Delgado trailing losses, and filter color terms. Using this apparent et al. (2014), Delgado & Reuter (2016), Yoachim et al. (2016), magnitude and the expected 5σ depth of each visit, the signal- LSST Science Collaboration et al. (2017), Jones et al. to-noise ratio (S/N) of the object in each visit is reported. In (2018, 2020), Naghib et al. (2019), and Bianco et al. (2022) addition, the probability of detection is also reported; this is and references therein. We provide a brief overview here. The close to requiring an S/N = 5 for detection but adds statistical Rubin Observatory scheduling software is part of rubin_sim scatter, which has the effect of smoothing the cutoff at 5σ, (Yoachim et al. 2022), an open-source Rubin-developed allowing occasional detection of fainter objects or occasional Python package. The rubin_sim package contains the losses of slightly brighter objects. primary LSST scheduling algorithm that will be used to The process of generating simulated small body populations choose pointings for the telescope based on real-time telemetry, is described in more detail, specifically for an NEO population, goal target maps, and configurable survey parameters. At the in Jones et al. (2018). For survey strategy evaluations, we top level, the scheduler uses a decision tree to generate include a range of sample populations from inner solar system observations in real time. The decision tree steps through the objects like NEOs, through mid-system objects like main-belt potential observing modes of (1) DDFs, (2) paired observations asteroids (MBAs) and Jovian Trojans, all the way to outer solar in a large contiguous area, (3) paired observations in twilight, system bodies like TNOs and comets. These include the and (4) single observations selected using a greedy algorithm. following: The DDFs are prescheduled for optimal times; all the other observing modes use a modified Markov decision process 1. NEOs based on a sample of orbits from Granvik et al. (MDP) similar to the one presented in Naghib et al. (2019) to (2018). A random set of 5000 orbits are drawn from the generate lists of desired observations. The MDP typically full sample of 802,000 synthetic NEOs instantiated by considers slew time, image depth, and desired footprint 41 Granvik and used for general NEO evaluation. In coverage when selecting potential observations. The schedul- addition, Earth minimum orbit intersection distance ing algorithm is paired with a model observatory to simulate (MOID) values were calculated for the full Granvik the full 10 yr LSST for these investigations. The model sample, and a subset of 5000 orbits with MOID values observatory includes a kinematic model of the telescope along <0.05 au were randomly selected to represent the with realistic weather logs, scheduled and unscheduled down- potentially hazardous asteroid (PHA) population. time, and a sky brightness model (Yoachim et al. 2016). 42 2. An ‘Ayló’chaxnim population, consisting of 10,000 Various survey strategy experiments are performed by either objects with orbits inside the orbit of Venus, was created modifying the scheduler decision tree (e.g., inserting a new via rejection sampling of the probability distribution for observing mode for taking high-airmass observations in twilight) or altering the MDP algorithm (e.g., adding a new 41 The full 802,000-object Granvik sample is available for download from basis function). https://www.mv.helsinki.fi/home/mgranvik/data/Granvik+_2018_Icarus/; the subset is selected as described in detail at https://github.com/lsst-sssc/ SSSC_test_populations_gitlfs/blob/master/MAF_TEST/granvik/Granvik% 2.2. Simulating Small Body Populations 20NEO%20Model.ipynb. Previously this population was referred to as the Vatira population or The movingObjects module in rubin_sim generates Vatiras (Greenstreet et al. 2012) before the discovery of the first known object the observations of a model small body population as the ‘Ayló’chaxnim (Bolin et al. 2020c, 2022; de la Fuente Marcos & de la Fuente objects would be seen in a particular simulated survey, taking Marcos 2020a; Greenstreet 2020; Popescu et al. 2020; Ip et al. 2022). 4 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. orbital elements given in the Granvik NEO model Table 1 (Granvik et al. 2018). Orbital elements were drawn from Rubin Colors for These Model SEDs the Granvik distribution and rejected unless they were compatible with the definition of Vatiras given in the Color (mag) S D C TNO same reference, i.e., objects between the the apocenter u − r 2.13 1.90 1.72 2.55 distance of Mercury (Q = 0.307 au) and the pericenter M g − r 0.65 0.58 0.48 0.92 distance of Venus (q = 0.718 au). Angular orbital V i − r −0.19 −0.21 −0.11 −0.38 elements not provided through the Granvik model were z − r −0.14 −0.30 −0.12 −0.59 y − r −0.14 −0.39 −0.12 −0.70 sampled from uniform distributions. To achieve reason- able statistical signal, this population is simulated with a Note. LSST catalogs will contain measurements reported as “top-of- larger sample size, as each individual orbit is inherently atmosphere” AB magnitudes. unlikely to be observed. 3. MBAs and Jupiter Trojans, based on a random sample of 5000 MBAs and 5000 Jupiter Trojan asteroids (respec- tively) from the Panoramic Survey Telescope and Rapid 1. The rough positions of the object at each night Response System (Pan-STARRS) Synthetic Solar System throughout the survey lifetime are calculated using Model (S3M; Grav et al. 2011). OpenOrb. 4. TNOs, based on a random sample of 5000 objects from 2. If the rough positions are within a tolerance value of any the L7 model from CFEPS (Canada–France Ecliptic visit in a simulated survey, a more precise position at the Plane Survey; Petit et al. 2011). time of that visit is calculated, along with the expected V- 5. Oort Cloud comet (OCC) populations, created from the band magnitude as calculated by OpenOrb for the H long-period comet model of Vokrouhlický et al. (2019). value recorded with the database (typically a fiducial Two different samples of 5000 comets are created, one placeholder value of H = 20 mag). with a maximum perihelion distance of 5 au and another 3. If the position at that time lands within the camera with a maximum perihelion distance of 20 au. footprint aligned with the boresight and rotation angle of For the comet populations, we include a cometary brightening the visit, the position is recorded as a potential function, based on the Afρ quantity of A’Hearn et al. (1984), observation. using a translation from H to cometary nuclei radii, and 4. The trailing loss and color term for that particular visit are parameters appropriate for long-period comets. Intrinsic light recorded (depending on the seeing of the visit, velocity of curves due to variations in shape of the objects or surface the object, the color of the object, and the filter used for albedo or color variations are not included for any population the visit). Solar system objects will be moving during but would be useful to include in the future. These populations LSSTCam exposures. Depending on the object’s velocity do not include every population across the solar system but and the observation’s exposure time, a solar system serve as a representative sample covering a wide range of object’s point-spread function (PSF) can appear extended apparent velocities, sky coverage, and orbital parameters for the along the direction of motion. Compared to a point source purposes of evaluating the impacts of changes in survey of the same apparent magnitude, a trailed source will strategy. have a lower S/N because the photons are spread across A variety of solar system reflectance spectra are assigned to more pixels on the detector. As a result, the Rubin the members of these populations, in order to determine color Observatory’s detection algorithm is not as sensitive to terms for the LSST filters. The general simple rule of thumb is trailed sources. The algorithm uses a stellar PSF-like that Bus-DeMeo (DeMeo et al. 2009) spectral energy matched filter to find sources in the LSST images that are distributions (SEDs) are assigned to objects depending on at or above the 5σ S/N detection limit. The trailing loss their semimajor axes; orbits with semimajor axes smaller than calculated in this step accounts for both the decrease in 2 au are assigned to S types, orbits with semimajor axes larger S/N and drop in detection efficiency compared to than 4 au are assigned to C types, and orbits between 2 and 4 au stationary point sources. We refer the reader to Section are assigned randomly to S versus C with a linear increase in 5.1.4 of Jones et al. (2018) for further details. probability as a function of semimajor axis, in accordance with 5. The series of potential observations are evaluated for an Ivezić et al. (2001). This means that ‘Ayló’chaxnims are array of H values. For example, the NEO population is entirely S type, the Trojans are entirely C types, while PHAs, evaluated for H values ranging from 16 to 28 mag, at NEOs, and MBAs are a mix of S and C types. The TNOs are steps of 0.2 mag. At H = 16 mag, the apparent magnitude assigned a significantly redder, TNO-specific SED, appropriate of the object that will be measured by the Rubin for the typical colors of a red dynamically excited TNO or a Observatory source detection pipeline in each visit is bluer object from the red cold classicals. The OCC populations calculated by combining the ephemeris V magnitude, the are assigned D-type SEDs, as a reasonable approximation for trailing losses, the color terms, and an offset between the the colors of the cometary nuclei. Colors for these populations fiducial H value and the current “clone” value of H = 16 are shown in Table 1. In reality, objects in each of these mag (for cometary populations, there is also a calculation populations show a range of colors, so this is a simplification of the cometary brightening).At H = 22 mag, the same but is sufficient for survey strategy evaluation purposes, as the process is repeated, but more of the potential observations same H-orbit-color distributions are applied to all the LSST of the object will fall below the 5σ S/N limit, so fewer cadence simulations used in this work. To illustrate this process more concretely, for each orbit in observations will be considered (and at a lower S/N) for the test population: the calculation of each metric for each object. 5 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 2 Key Solar System MAF Metrics Used in This Analysis Population Main Metrics Discovery Metrics a,b ‘Ayló’chaxnims 3 nightly pairs in 15 nights discovery completeness for H „ 16.0 3 nightly pairs in 15 nights discovery completeness for H „ 20.5 4 detections in 1 night discovery completeness for H „ 16.0 4 detections in 1 night discovery completeness for H „ 20.5 PHAs 3 nightly pairs in 15 nights discovery completeness for H „ 16.0 3 nightly pairs in 15 nights discovery completeness for H „ 22.0 NEOs 3 nightly pairs in 15 nights discovery completeness for H „ 16.0 3 nightly pairs in 15 nights discovery completeness for H „ 22.0 MBAs 3 nightly pairs in 15 nights discovery completeness for H „ 16.0 3 nightly pairs in 15 nights discovery completeness for H „ 21.0 Jupiter Trojans 3 nightly pairs in 15 nights discovery completeness for H „ 14.0 3 nightly pairs in 15 nights discovery completeness for H „ 18.0 TNOs 3 nightly pairs in 15 nights discovery completeness for H „ 6.0 3 nightly pairs in 15 nights discovery completeness for H „ 8.0 OCCs with q „ 5 au 3 nightly pairs in 15 nights discovery completeness for H „ 8.0 3 nightly pairs in 15 nights discovery completeness for H „ 17.0 OCCs with q „ 20 au 3 nightly pairs in 15 nights discovery completeness for H „ 8.0 3 nightly pairs in 15 nights discovery completeness for H „ 12.0 Light-curve metrics PHAs Fraction of H = 16.0 with sufficient observations for light-curve inversion Fraction of H = 19.0 with sufficient observations for light-curve inversion NEOs Fraction of H = 16.0 with sufficient observations for light-curve inversion Fraction of H = 19.0 with sufficient observations for light-curve inversion MBAs Fraction of H = 16.0 with sufficient observations for light-curve inversion Fraction of H = 18.0 with sufficient observations for light-curve inversion Jupiter Trojans Fraction of H = 14.0 with sufficient observations for light-curve inversion Fraction of H = 15.0 with sufficient observations for light-curve inversion Notes. In the figures presented in this work, these metrics are normalized and compared to the baseline simulation for a range of cadence simulation families by varying a different survey strategy parameter. Previously referred to in the literature as Vatiras. Metrics for the ‘Ayló’chaxnims are only analyzed for simulation families that include low-SE twilight observations. Only assessed for simulations that take four observations per pointing during twilight. Metrics for OCCs are only calculated since the v2.0 simulations. 6. The result is a series of values for each metric, Generally speaking, our current solar system science metrics corresponding to the combination of the positions can be split into two categories: discovery metrics and resulting from each orbit with the apparent magnitudes characterization metrics. Discovery metrics relate to which resulting from a range of H values. objects could be discovered in the survey, while characteriza- tion metrics cover a broad range of science areas such as This is repeated over all of the orbits in the test population. likelihood of detecting activity on the surface of an object or likelihood of acquiring a color measurement. For each metric, the value per orbit−H magnitude combination is calculated and 2.3. LSST Solar System Science Metrics recorded, and then a “summary value” is evaluated across the With the movingObjects and MAF modules of rubin_- entire population. For discovery metrics, this summary value is sim, the calculation of any arbitrary quantity per object is the fraction of the population that can be linked by Rubin straightforward. The MAF software identifies the observations Observatory’s Solar System Processing (SSP) pipelines (Myers of a given object (or more specifically, orbit and H value, in the et al. 2013; Jurić et al. 2020)—the discovery completeness. For case of cloning) and passes these to the MAF Metric, where characterization metrics, the summary value is typically the the value can be calculated based on the acquired observations fraction of the population that meets a given threshold—the and then saved. Summary values across the entire population, fraction of the population that is likely to meet light-curve such as fraction of objects with light-curve inversion potential inversion requirements, for example—although it can also be or “discoverable” objects, can be calculated from the results. the mean or median or maximum (etc.) value of the metric 6 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 3 Secondary Solar System MAF Metrics Used in This Analysis Population Secondary Metrics Color Light-curve Metrics PHAs Fraction of H = 16.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy Fraction of H = 19.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy NEOs Fraction of H = 16.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy Fraction of H = 19.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy MBAs Fraction of H = 16.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy Fraction of H = 18.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy Jupiter Trojans Fraction of H = 14.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy Fraction of H = 15.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy TNOs Fraction of H = 6.0 with at least 30 S/N > 5 observations in 1 filter and 20 observations in 3 other filters Fraction of H = 8.0 with at least 30 S/N > 5 observations in 1 filter and 20 observations in 3 other filters OCCs with q „ 5 au Fraction of H = 8.0 with at least 30 S/N > 5 observations in 1 filter and 20 observations in 3 other filters Fraction of H = 17.0 with at least 30 S/N > 5 observations in 1 filter and 20 observations in 3 other filters OCCs with q „ 20 au Fraction of H = 8.0 with at least 30 S/N > 5 observations in 1 filter and 20 observations in 3 other filters Fraction of H = 12.0 with at least 30 S/N > 5 observations in 1 filter and 20 observations in 3 other filters Notes. In the figures presented in this work, these metrics are normalized and compared to the baseline simulation for a range of cadence simulation families by varying a different survey strategy parameter. Metrics for OCCs are only calculated since the v2.0 simulations. across the population. These summary values are reported at each pair; in the default configuration, the minimum time either a particular H value or cumulatively, for objects with H separation was set to 0 minutes, and the maximum time less than or equal to a given H value. These summary values at separation was set to 90 minutes, corresponding to the both a bright H (large size) and a fainter H (smaller size) are approximate limits suggested by early expectations for pulled out for each population for comparison of multiple configuration for the solar system processing pipelines and simulations. The particular H values used are dependent on the very widely bracketing the typical expected separation of visits. population; typically the bright H value is where the metric The overwhelming majority of visits in the survey are acquired results reach their highest value and then remain constant with in pairs with a separation of 22–30 minutes (depending on the decreasing H. The fainter H values are typically set close to details of the survey configuration); the pairs of visits are where the baseline survey strategy reaches about 50% for that usually acquired in “adjoining” filters (i.e., g and r or r and i metric result. The discovery and characterization metrics used visits for a pair); and, coupled with the large FOV of Rubin, in this paper are listed in Tables 2 and 3. The details of how most although not all observations of an object are followed up these metrics are calculated are described below in by a second observation in the same night. It is also helpful to Sections 2.3.1–2.3.3. consider objects that could be discovered via more traditional methods of identifying four observations on the same night 2.3.1. Discovery Metrics (i.e., “quad detections”). This is particularly useful when considering observations of near- or interior-to-Earth asteroids The SSP pipelines (Myers et al. 2013; Jurić et al. 2020) will within the special near-Sun twilight microsurvey, where link transient sources from the nightly visits into “tracklets” observations are purposefully obtained in quads in order to (potential linkages in the same night using linear extrapolation). secure identifications of these rapidly disappearing asteroids. If SSP will identify new moving objects by attempting to link the observations of a given orbit−H combination meet the together three tracklets from within a 15-day window onto a required criteria at least once, the object is considered heliocentric orbit. The current baseline LSST object discovery “discovered”; to compare the results across different simula- guidelines require pairs of observations on three separate tions of survey strategy, the discovery completeness is reported nights, within a window of 15 days as the design goal and 30 at both a bright and faint H value for each population. More days as the stretch goal; the 15-day requirement is a confident details about the discovery metrics are presented in Jones et al. lower limit, but a 30-day window is a reasonable extension that (2018), including more background about the potential for is also useful to consider. Thus, the basic discovery metric false-positive discoveries. In short, we do not expect a searches for precisely this: pairs of observations on at least significant number of false-positive detections, regardless of three different nights within 15 or 30 days, using the survey strategy choices, with the criteria of three pairs of probabilistic detection value to determine what is visible or detections over a window of either 15 or 30 nights; this is due not. The metric allows for setting the minimum and maximum time separation between the individual visits in The minimum time separation for pairs of visits was set to 0 minutes during the metric runs analyzed in this paper. In the future, we will be using 5 minutes The probabilistic detection likelihood depends on the expected 5σ point- as the minimum separation time. However, we do not anticipate there being a source depth, determined by sky brightness, air mass, and seeing alone; it does significant drop in metric performance, as the overwhelming number of pairs of not take into account potential crowding in the field. visits are acquired at very close to the goal time separation, around 33 minutes. 7 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. to a range of factors, including the low fraction of false-positive asteroid from photometric measurements over a wide range of detections coming from difference imaging and the low viewing geometries as suggested in LSST Science Collabora- likelihood of pairs of detections on three separate nights tion et al. (2009; e.g., Hanuš et al. 2011; Ďurech et al. 2016; aligning within expected residuals for initial orbit Muinonen et al. 2020). The light-curve inversion metric determination. evaluates the suitability of a set of observations for this The various survey strategy simulations and populations process. The evaluation is based on the phase curve and ecliptic expose some basic trends: longitude coverage provided by the observations, as well as the overall number and S/N of each observation, considering 1. Discovery completeness for slow-moving populations, observations in a single filter at a time. The ecliptic such as TNOs, depends strongly on the total area longitude range of the observations must be more than 90° included in the survey. Because these objects move so ecliptic longitude and cover more than 5° of phase angle, as a slowly year over year and are relatively “easy” to proxy of the required range of viewing geometries. Further, discover via linking, the footprint itself is the most there must be more than a threshold value of S/N-weighted important consideration of the survey strategy, particu- observations, equivalent to about 50 S/N = 100 observations larly for the brighter TNOs. The completeness for the or 250 S/N = 20 observations, all in the same filter, in order to fainter TNOs can also vary slightly depending on which provide enough photometric measurements. Like all other filters are paired together in visits and whether the most metrics within MAF, the rotation of the asteroid and its impact sensitive filters are used often enough within the window. on the photometric measurements is not considered; presum- 2. Discovery completeness for fast-moving populations, ably this would be part of the light-curve inversion process. If such as NEOs, depends more strongly on the number all conditions are met, then light-curve inversion is at least of visits per pointing. Since NEOs travel across much potentially likely; thus, this metric provides a likely upper limit more of the sky on the timescale of the survey, the on the fraction of the population for which light-curve footprint is not as much of a constraint as for TNOs. inversion may be possible. This is evaluated per orbit−H However, the total number of visits in the survey is combination, and then the fraction of the population (at a bright relatively constant with different survey strategies, and so and fainter H value) is reported. Outer solar system objects the footprint influences the number of visits per pointing never achieve the required range of viewing geometries, and and thus the typical cadence of those visits. Fainter NEOs objects where the nucleus is shrouded with coma such as active in particular may only be visible for a short period of comets are also not good candidates; this metric is not time; thus, more visits per pointing result in a higher evaluated for these populations. likelihood of an object having observations suitable for This metric is very sensitive to the number of observations discovery, and so a higher discovery completeness. For per pointing, but also to the cadence of those observations. the brightest NEOs, the footprint weighs in as well, as Generally, we find a trend across the simulations that the light- covering more sky results in discovering more NEOs. curve inversion results track in a similar sense for all of the 3. Intermediate populations, such as MBAs, fall in between inner solar system populations, with NEOs being least sensitive these extremes. In general, we find a threshold number of to survey strategy variations, followed by PHAs, then MBAs, visits per pointing results in good completeness for a and finally Trojan asteroids showing the most variation in given population, and this threshold increases as the H metric results as survey strategies change. value being evaluated gets larger and/or the population includes more small semimajor axis or high-inclination or high-eccentricity orbits. 2.3.3. Color Light-curve Metrics 4. The Jupiter Trojans show stronger variability with some There are several metrics relating to determining colors for kinds of survey strategy changes that include changes in the small body population members, tailored for inner solar the timing of observations. Some survey strategies focus system or outer solar system objects. As the LSST will not visits on particular regions of the sky in particular years, obtain instantaneous colors, each of these metrics also includes such as in the rolling cadence. These variations can result some requirement on measuring a light curve. in a higher or lower sampling of the Jupiter Trojan For the inner solar system, the color light-curve metric population depending on the timing of visits, as these evaluates the number of S/N-weighted observations per asteroids are both more spatially constrained and moving bandpass to evaluate whether the color could be determined across the sky. in that bandpass. Essentially, this could be translated to fitting 5. More relaxed discovery criteria result in more discov- the light curve in each bandpass alone and then combining eries, but with similar trends. For example, 30-day these light curves to evaluate the color. The equivalent of windows perform about 2%–5% better than 15-day 40 S/N = 5 detections or 10 S/N = 20 detections per filter are windows for fainter objects, depending on the population required, but the more extensive requirements that relate to (brighter objects show little difference). However, these achieving a range of viewing geometries for light-curve different criteria follow similar trends between survey inversion are not, and no limitation is set on when the strategies, meaning that evaluating 15-day windows observations are acquired. The specific number of detections shows similar preferences in survey strategy to evaluating needed is based on an estimate of the amount of data that would 30-day windows. be sufficient to measure basic light-curve, color, and phase- curve parameters with scientifically meaningful uncertainties. Although work is still needed to use the sparse LSST-like 2.3.2. Light-curve Metrics cadence to determine these parameters, a preliminary assess- Inner solar system objects have the potential to be subjects ment suggests that 20–40 observations per color should be for sparse light-curve inversion, inferring the shape of the sufficient. While phase curves are also necessary for this 8 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. analysis, we elected to keep the metric simple by not requiring No ISOs were simulated for this work. With only two ISOs a particular spread in phase angles. In practice, almost any known to date (Meech et al. 2017; Borisov 2019), the cadence will produce sufficient constraint on the phase curve to characteristics of this population are currently unconstrained. allow for colors to be determined for the vast majority of Long-period comets are distributed across the sky with a much objects. The summary values reported are the fraction of the larger range of ecliptic latitudes compared to the MBAs and population (at a given H value) for which two specific colors TNOs, due to the effects of passing stars and Galactic tides that (g − r or g − i plus g − z or g − y), three specific colors (g − r, shape the Oort Cloud into a shell rather than a flared disk shape r − i, i − z or r − i, i − z, z − y), four colors (g − r, r − i, i − z, (Everhart 1967; Fernández 1997; Francis 2005; Higuchi et al. z − y), or all five colors (adding u − g to the four-color set) are 2007; Brasser et al. 2010; Dones et al. 2015; Vokrouhlický potentially determined. et al. 2019; Higuchi 2020, and references therein). Recent For the outer solar system, a slightly different color light- predictions by Engelhardt et al. (2017), Seligman & Laughlin curve metric evaluates the number of observations reaching a (2018), and Hoover et al. (2022) suggest that LSST ISO minimum threshold (S/N ≈ 5). This metric requires at least 30 discoveries will cover a wide range of ecliptic latitudes and observations in a “primary” bandpass and then 20 observations heliocentric distances similar to long-period comets. Thus, we in the additional bandpass(es). This is equivalent to assuming a assume that the trends seen for the simulated LSST OCC light-curve fit in the primary bandpass with additional discoveries can provide some broad guidance for how the observations in the secondary bandpass serving to help fit the cadence decisions will impact LSST ISO discoveries. Like 1I/ light curve and color, possibly simultaneously (such as would ‘Oumuamua, which was discovered at 0.22 au (Meech et al. be possible with multiband Lomb–Scargle fitting). The 2017) moving at 6°.2 per day, a subset of ISOs discovered close summary values reported are the fraction of the population to Earth will on short timescales (10 days) look similar to (at a given H value) for which one, two, or more colors can be NEOs (e.g., Cook et al. 2016). Therefore, the NEO metrics are fitted, without restrictions on which bandpasses are used. also insightful for gauging the potential impacts to the ISO discovery rate. The solar system MAF metrics assume equal detection 2.3.4. Metric Limitations efficiency across all areas of the survey footprint (even near the plane of the Galaxy, where stellar crowding may be a factor). As described above in Section 2.2, the most accessible and Rubin Observatory’s data pipelines will detect solar system up-to-date orbital and absolute magnitude distributions have bodies using difference imaging. Templates representing the been used to model the expected LSST solar system detections. static sky will be subtracted from the nightly images, and what The physical and orbital properties of the modeled synthetic remains will be a variable, transient, or moving source. This small bodies are driven by observational data, but the LSST will help significantly in detecting solar system objects in cadence simulations do have to make some assumptions about regions of high stellar density, but stellar crowding will likely these small body populations. This is particularly true on the cause some decrease in the efficiency of Rubin Observatory’s smallest size scales that have not been very well probed by past difference image analysis (DIA) and SSP pipelines. The MAF wide-field surveys. The distribution of different surface types solar system metrics are likely overly optimistic near and in the applied to the various simulated small body reservoirs will also Galactic plane, where stellar crowding is the highest. This impact the apparent magnitude of the synthetic objects in the should be kept in mind when examining the cadence various optical filters. Additionally, we have to make simulations modifying the LSST Galactic plane observing simplifying assumptions about active objects. We assume that strategy. all comets will generate dust coma with the same relation The Rubin scheduler aims to take image pairs, each night per applied to calculate the observed apparent magnitude, and the pointing, to facilitate the identification of moving solar system effects of cometary outbursts are ignored. In addition, rotational objects (Ivezić & the LSST Science Collaboration 2013). The brightness variations due to shape or uneven surface albedo are time between these repeat observations is a tunable survey not accounted for in these simulations. Thus, the exact number parameter. The Rubin SSP pipelines (Myers et al. 2013; Jurić of solar system minor planets found by LSST will differ from et al. 2020) require motion within a single night for initial that “discovered” in the simulations explored in this paper discovery. Transient sources that appear stationary between the because of these choices. two images taken on the same night will not be included in the The smaller solar system minor planet populations, such as daily tracklets that the SSP algorithm will try to link with the main-belt comets (MBCs), Jupiter-family comets (JFCs), tracklets from previous nights. The Rubin SSP pipelines as sungrazing comets, Neptune Trojans, and Centaurs, have not currently planned will not be able to detectbodies beyond been simulated for this work. For the MBCs, sungrazing ∼100–150 au (see Section 4.4.1 for a detailed estimate), but comets, and JFCs this is partly due to having to develop a other search algorithms will likely be developed by the wider representative activity model. We can instead use the community to search for very slow moving objects in the LSST populations that are simulated in the rubin_sim simulations data. The MAF solar system discovery metric does not account as proxies to help inform what the impact of various cadences for SSP’s slow motion limit. The only metrics really impacted might be. Simulated survey strategies that will improve the by this are the estimated TNO discoveries. As long as the metrics for MBAs and NEOs will also likely enhance the median separation between the observations is similar for a set discovery and monitoring of MBCs and JFCs. Cadences that of cadence simulations, then the output from the discovery improve the chances of finding near-Sun ‘Ayló’chaxnims will likely increase the LSST discovery rate of sungrazing comets, metrics can be compared. We note that some care must be like the Kruetz family. As the Centaurs reside in the middle taken when considering the impact of varying the time solar system, the impacts on the Centaurs can be extrapolated separation between repeat observations, and we refer the using the TNO and MBA simulation metrics. reader to Section 4.4.1 for further discussion. 9 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. There are currently no MAF metrics that measure how runs were redone with an update in the scheduler configuration precise small body orbital predictions and characterization will after the submission of this paper owing to an issue with the be based on Rubin Observatory observations. The accuracy of sky distribution of u-band observations. We use the updated the orbits of moving objects is primarily driven by the v2.2 simulations in our analysis. The v1.5−v2.2 cadence observational arc length. There were no reasons to consider simulations are described in detail in Jones et al. (2020) and the observational arc length separately with a dedicated MAF Yoachim (2022). Short descriptions of the simulations are also metric because all of the observing strategy options currently available in online Jupyter notebooks. The resulting MAF being considered as part of the LSST cadence optimization metrics derived from these simulations are available in online exercise (see Section 3) have repeat coverage of the entire CSV (comma-separated values) files. LSST footprint over several years. This should be sufficient for We focus in this paper on the key survey strategy parameters the needs of the majority of astrometric and dynamical solar that drive significant changes in the detectability and system science cases. We also note that if a cadence option not characterization of solar system objects or would lead to covering the entire sky over the majority of the 10 yr time span unique planetary astronomy data sets that only Rubin were evaluated, it would be undesirable for other science cases Observatory could provide. Several of the simulation families such as proper-motion measurements. were repeated in later versions with improvements to the Rubin The likelihood of having satellite streaks and glints present scheduler, changes to the prescription used in the scheduler, or in LSST images is increasing with every satellite constellation modifications to the planned survey footprint. For this work, if launch (e.g., Starlink, Project Kuiper, and OneWeb). The a simulation family was repeated in later releases, we only impact of future satellite constellations is not currently taken review the latest version. We also note that the OCC orbital into account by the metrics. We discuss the potential impacts of distributions were only incorporated as MAF metrics in release the ongoing industrialization of the near-Earth environment in 2.0 and onward. We include the OCC metrics when available. Section 5.4. The v2.1 simulations include a range of families that explore Keeping these caveats in mind, the LSST cadence simula- the final details of the DDF observing strategy. No solar system tions and the MAF metrics can be used to explore the impact of metrics were run against these v2.1 DDF families, as very small various changes to the LSST observing strategy. Some care is numbers of solar system objects will be discovered in these required in examining certain families of simulations. Overall, fields compared to the rest of the survey footprint as a result of by adopting the same synthetic small body populations for each the fact that the DDFs take 5% of the observing time at of the cadence simulations and focusing on the relative change locations high off the ecliptic. The main lever arm for solar in the MAF metrics compared to the baseline survey, we can system science in relation to the DDFs is the fraction of total still gain a good understanding of the impact caused by tuning observing time spent on the DDFs, which is explored in various LSST observing parameters. Section 4.6. Simulations covering rotational and positional dithers between repeat survey pointings are also not explored 3. Overview of the LSST Cadence Simulations here because of the negligible impact on the solar system (Versions 1.5–2.2) metrics. Over the past several years, a variety of LSST cadence Appendix B (Table 8) gives a brief overview of the LSST simulations have been generated (e.g., LSST Science Colla- cadence simulations evaluated in this paper. The LSST cadence boration et al. 2009, 2017; Ivezić et al. 2019; Jones et al. 2020; simulations can be divided into several broad categories or Yoachim 2022) exploring various avenues for optimizing the families exploring different modifications to the survey WFD survey and exploring different scenarios for what to do footprint, filter distribution, intranight visits, DDF observing with the remaining ∼10%−20% of survey time. We examine strategy, visit exposure times, rolling cadence strategies, and the LSST cadence simulations produced after the implementa- microsurveys. Each simulation family explores changing one tion of the Feature Based Scheduler system (Naghib et al. parameter in the LSST observing strategy. The footprint 2019), as this iteration of the Rubin scheduler is closest to the families explore the shape and location of the WFD on-sky version that will be in place during survey operations, starting footprint, as well as the possible adoption of a variety of with the version 1.5 simulation release. At the time of this minisurveys, strategies surveying the sky outside the WFD paper’s submission, additional families of simulations have footprint or with a different cadence to the WFD that require a been released up to version 2.2. The v1.5 simulations were few percent or more of the total available LSST observing time. released in 2020 May, version 1.6 in 2020 August, v1.7 in One such example of a minisurvey is observing the northern 2021 January, and v1.7.1 in 2021 April. These simulations ecliptic region. Microsurveys are small observing campaigns cover a wide range of variations of the survey strategy that requesting ∼0.3%–3% of the total observing budget. Rolling informed the first round of the SCOC’s review. After assessing cadence in this context focuses on prioritizing observing some the feedback from the Rubin user community, the SCOC parts of the sky over others in order to acquire more recommended a new round of simulations (v2.0) to inform their photometric data points in a given observing season. This final deliberations (Ivezić & the SCOC 2021; Bianco et al. enables faster and better identification of supernovae, kilo- 2022). The v2.0 cadence simulations were made available in novae, and other astrophysical transients (LSST Science 2021 November. Two additional smaller sets of simulations Collaboration et al. 2017; Yoachim 2021). were released in 2022 April and June (v2.1 and v2.2) that clarify/explore some limited options identified after commu- https://github.com/lsst-pst/survey_strategy/blob/main/fbs_1.7/ nity review of the 2.0 cadence simulations, including DDF SummaryInfo.ipynb and https://github.com/lsst-pst/survey_strategy/blob/ observing options, new parameters for implementing the main/fbs_2.0/SummaryInfo_v2.1.ipynb. twilight low-SE solar system observations, and revised https://github.com/lsst-pst/survey_strategy/tree/main/fbs_1.7 and https:// scenarios for Galactic plane observing. The v2.2 simulation github.com/lsst-pst/survey_strategy/tree/main/fbs_2.0. 10 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 1. Metric values for the primary metrics under consideration for baseline_v2.1_10yrs from the latest version of rubin_sim. These values represent the fraction of the simulated population that would “pass” the metric requirements. “Completeness” refers to the discovery completeness for each sample population at the indicated H value, while “Fraction LC Inversion” refers to the fraction of each population that would have observations that meet the metric requirements, implying that that object would be a good subject for light-curve inversion. Likewise for “Fraction 4 filters,” showing the fraction of each population that would be likely to obtain colors in four filters. Full descriptions of the metrics are listed in Tables 2 and 3. Numerical values are provided in Appendix C. 4. Impact of Survey Strategy Choices in Table 2. We focus our analysis on the discovery metric that best matches the SSP discovery requirements (three tracklets How to evaluate whether a specific LSST survey strategy is detected within 15 nights), as other variations of the discovery “good” or “bad” for solar system science has a complex metrics require bespoke community-developed software tools. answer. How does one weigh a significant improvement in In a small number of instances reviewing the color light-curve NEO discoveries to a large loss in the number of TNOs found? metrics calculated for four colors was also useful for It depends on which population one is interested in studying interpretation (see Table 3 for input parameters), but we will and on the science goals one wants to achieve. We choose a primarily focus on the discovery and light-curve inversions for unified approach when evaluating the various LSST cadence this work. When examining a given cadence experiment, we simulations. We equally consider the impact on the main solar normalize all the metric values calculated to the relevant system populations probed by LSST: NEOs, PHAs, TNOs, baseline cadence or reference simulation that we consider the MBAs, ISOs, and OCCs. Secondary consideration is given to default scheduler parameter setting or configuration for this the smaller populations such as giant planet Trojans and inner- cadence experiment. See Figure 4 for an example where the Earth objects (IEOs; objects on orbits interior to Earth’s orbit). resulting solar system metrics for discovery (top) and light- Although an ISO population is not simulated for this work, we curve inversion (bottom) are presented. We note that the Jupiter use the OCC and NEO metrics where appropriate to examine Trojans have the most variable metrics owing to their smaller the impact on the ISOs in the various cadence simulations (see numbers and constrained positions on the sky. Metric results Section 2.3.4). The SSSC Science Roadmap (Schwamb et al. for the most recent baseline survey simulation at the time of 2018a) sets out the collaboration’s science priorities with LSST submission (baseline_v2.1_10yrs) are shown in data. The document was designed specifically to guide future Figure 1 and listed in Appendix C (Table 9). cadence decisions and ranks the key solar system research We deem reductions in the relevant metrics larger than ∼5% themes for investigation with LSST. Based on the SSSC unsuitable. The small body science goals set out in the SSSC Science Roadmap, for each LSST cadence simulation we Science Roadmap (Schwamb et al. 2018a) are derived from evaluate in priority order the impact on (1) discovery/orbital increasing sample sizes by an order of magnitude. This ∼5% characterization, (2) color measurements, and (3) rotational threshold prevents a “death by a thousand cuts” scenario where light curves. all the tuned cadence parameters produce individually small We have found that per small body population the light- impacts on the metrics but when combined cause a significant curve inversion and discovery metrics sufficiently encapsulate reduction in solar system science. This constraint also buffers the requirements for obtaining reliable broadband colors, such against any future unexpected small observing time losses. We that the majority of cadence simulation families are evaluated have provided written feedback to the SCOC identifying which using these two metrics alone. The main metrics used in our cadence simulations pass or fail our criteria (are “good” or analysis and the parameters used in their calculation are listed “bad” for solar system science). In this paper, we will not 11 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 2. The total number of visits in all filters after 10 yr for a representative sample of LSST simulations. In several of these scenarios the effective on-sky footprint of the WFD survey and other observing areas, including the NES, Galactic plane (GP), and south celestial pole (SCP) regions, change depending on how the observing time on-sky is divided. The DDFs are also visible as a collection of single fields receiving a higher number of observations than a WFD pointing, with each DDF receiving approximately 1% of the total LSST observing time. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. identify every simulation that fails our thresholds, as this can be 4.1. Survey Footprint readily identified using the relevant figures within the following The LSST footprint determines what sky is observed over sections and the MAF output. Instead, we focus on examining the 10 yr survey and how the total number of on-sky visits gets the trends in the solar system metrics as each knob is turned apportioned across the major components of the LSST. and providing recommendations based on this analysis. 12 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 3. The footprint of the NES in equatorial coordinates. The light-blue shading represents the pointings requested as part of the NES. The solid black line represents the ecliptic. The dashed black lines represent ±10° ecliptic latitude. The solid blue line plots the center of the Galactic plane. The dashed blue lines mark ±10° Galactic latitude. The plot is centered on α = 0 and δ = 0. R.A. is marked every 30°, and decl. lines are visible every 15° up to and including ±75°. Examples can be seen in Figure 2, which depicts a solar system populations like the MBAs will complete full representative set of footprints explored in the v2.0–v2.1 orbits. This means that MBAs in the northern hemisphere at the simulations. In this section, we focus on the arguments for start of the survey will be in favorable positions to be detected incorporating the northern ecliptic region into the LSST with within the WFD during the later years of the survey. This is not the Northern Ecliptic Spur (NES) minisurvey. We also examine true for outer solar system objects whose orbital periods are the amount of observing time that should be divided between well beyond ∼10 yr. Outer solar system bodies will only have a the Galactic plane and NES minisurveys and options for the small fraction of their orbital periods covered by the LSST. shape and extent of the WFD footprint. Later sections will Thus, the vast majority of TNOs located in the NES at the start discuss variations on how these visits are executed, such as of the survey will remain in the northern hemisphere, missing how they are distributed over time (Sections 4.4 and 4.5) and the WFD footprint. This is reflected in Figure 4, where the first by filter (Section 4.3). Small modifications to the footprint two simulations plotted are baseline_v1.5_10yrs, which using much less than a few percent of the observing time are includes the NES minisurvey, and filterdist_indx2_- presented in the microsurvey discussion in Section 4.7. v1.5_10yrs, which excludes the NES. TNO discoveries suffer nearly a 30% loss with the exclusion of the NES minisurvey, while there is only a very small drop for the inner 4.1.1. Northern Ecliptic Spur solar system populations. Although not simulated at the time in The WFD by its design requirements is meant to cover the this cadence experiment, populations that are more uniformly majority of the sky in the southern celestial hemisphere below distributed on the sky (such as OCCs and ISOs) also benefit 0° decl. (Ivezić & the LSST Science Collaboration 2013), but from the inclusion of the NES, which provides additional sky the Simonyi Survey Telescope is capable of observing the coverage and therefore more chances for discovery. entire ecliptic. The extent of the WFD has evolved over time Figure 4 also shows that the light-curve metrics for small (as shown in Figure 2 and later discussed in Section 4.1.2), but MBAs suffer a bit more than a 15% loss when the NES no matter what the proposed variations to the WFD sky minisurvey is not executed. Discovery relies on the object coverage are, the full extent of the ecliptic plane will not be being above the 5σ limiting magnitude on three nights, but to incorporated into the WFD footprint. The NES minisurvey perform light-curve inversion requires many more observa- aims to remedy this situation by ensuring that higher-airmass tions. The NES provides additional opportunities to observe observations of the northern ecliptic are taken as part of the those faint objects close to the LSST limiting magnitude, LSST (LSST Science Collaboration et al. 2009, 2017; giving additional chances for the small MBAs to be observed in Schwamb et al. 2018b; Ivezić et al. 2019; Bianco et al. conditions where they might have sufficient S/N to contribute 2022). The NES region, shown in Figure 3, is composed of to shape modeling. The opposite effect is observed for the ∼604 pointings covering in total ∼5800 deg spanning from 0° small PHAs and NEOs, which benefit in simulations without decl. to +10° ecliptic latitude. In order to make this goal the NES minisurvey (about a 6%–10% increase in the light- achievable with the non-WFD time, the NES minisurvey has curve inversion metrics). These objects are typically detected typically been implemented in the cadence simulations to close to Earth and so quickly become too faint to be detected. receive a smaller number of visits per field (∼250; as shown in Thus, pushing the time used for the NES minisurvey into Figure 2) compared to the WFD. The NES minisurvey includes additional WFD visits enables more observations where these observations taken in a combination of the griz filters only, small PHAs and NEOs are detectable and can have light curves where solar system objects are typically the brightest. The measured. The opposite effect can be seen for the larger PHAs observing time dedicated to the NES is explored in and NEOs. The larger PHAs and NEOs suffer a ∼10% drop in Section 4.1.3; here we focus on the impact of including or the light-curve metrics when the NES fields are excluded. excluding the NES minisurvey from the LSST. The NES minisurvey is crucial for inventorying the outer Because large PHAs/NEOs are more likely to be above the solar system. About half of the ecliptic plane is covered within limiting magnitude in an LSST image, surveying the NES the WFD footprint. Over the 10 yr span of the LSST, inner creates new opportunities to monitor the brightness of large 13 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 4. Possible tuning options for the LSST footprint from the v1.5 experiments. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. We have truncated the bottom panel’s y-axis for visibility. The change in the H = 15 Jupiter Trojan detections in some of the runs extends well above 1.2. PHAs/NEOs as they pass by Earth. The Jupiter Trojans also partially sampled without the NES observations, as discussed in Schwamb et al. (2018b). Two such cases are the Neptune take a significant hit when the NES is not included. This is likely due to their constrained positions on the sky. Trojans and the resonant TNO populations. Over the 10 yr Not captured in the MAF metrics are the benefits that the period, the vast majority of the leading Neptune Trojan L4 NES minisurvey provides to small body populations that are cloud is accessible only via observations of the NES as shown distributed asymmetrically across the sky. They would only be in Figure 5. Lin et al. (2019) find evidence for potential 14 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 5. The sky positions of the Neptune Trojan population from the Lin et al. (2021) model on 2025 June 22 (gold) and 2033 June 22 (gray). The two epochs represent early and late times in the LSST, respectively. The leading L4 cloud is at north, left-hand side of the plot. The dashed black lines represent ±10° ecliptic latitude. The solid blue line plots the center of the Galactic plane. The dashed blue lines delineate ±10° Galactic latitude. Probing the low-inclination L4 Neptune Trojans requires the inclusion of the NES in the LSST footprint. differences in the color distributions of the L4 (leading) and L5 enough to be visible with LSST images. If Planet Nine is bright (trailing) Neptune Trojans. The WFD and NES minisurvey enough to be imaged in single LSST exposures, the length of combined are capable of sampling both clouds with sufficiently the survey in combination with the repeated coverage of the large numbers to test this further. Including the NES region in survey footprint effectively eliminates the possibility of the LSST footprint enables characterization of the libration missing Planet Nine in high Galactic latitude fields owing to islands for the various mean motion resonances (MMRs) with coincidental overlap with another source. Closer to the Galactic Neptune (Schwamb et al. 2018b). Only observing half the plane, stellar crowding will be significant and identifying ecliptic with just the WFD and Galactic plane minisurvey sources will be difficult. This may require community- would impact the study of the resonant TNO populations, optimized search algorithms to look for Planet Nine in these which preferentially come to perihelion at certain locations on observations. Rubin Observatory is also exploring additional the sky (e.g., Gladman et al. 2012; Gladman & Volk 2021). options to enhance source extraction near the Galactic plane The NES minisurvey is crucially important for searching for (see Bosch et al. 2019). If current searches fail to find Planet additional distant planets in the solar system and testing the Nine, Rubin Observatory will put the best observational apparent orbital clustering of Sedna-like inner Oort Cloud constraints on the existence of Planet Nine over the next objects (IOCs; q > 50 au and a > 250 au) and extreme TNOs decade and will be the facility with the best chance of directly (ETNOs; objects on orbits with q > 42 au and a > 150 au).It imaging it (Trilling et al. 2018b). As noted in Section 2.3.4, has been proposed that a giant planet (“Planet Nine”) is Rubin Observatory’s SSP pipelines are only sensitive to gravitationally shepherding the distant planetesimals onto moving objects at heliocentric distances 100–150 au. We similar orbits with aligned orbital poles and longitudes of fully expect that there will be several community-led efforts to perihelion (Trujillo & Sheppard 2014; Batygin & Brown 2016; find very slow moving distant objects in the LSST transient Sheppard & Trujillo 2016; Batygin et al. 2019; Brown & catalogs to search for Planet Nine and explore the IOCs and Batygin 2019, 2021; Oldroyd & Trujillo 2021). Recent ETNOs. Therefore, it is still important to consider this science modeling by Brown & Batygin (2021) combined with case for LSST footprint considerations. constraints from the Zwicky Transient Facility (ZTF; Brown Even if Planet Nine is not visible in the LSST images, the & Batygin 2022) and the Dark Energy Survey (DES; Belyakov LSST would potentially be able to reveal its presence if the et al. 2022; Bernardinelli et al. 2022) predict Planet Nine to be orbital alignment holds with the increased LSST sample of residing at a semimajor axis of 700 au or higher. Although the ETNOs and IOCs and matches the Planet Nine predictions. current predictions made available in Brown (2022) do have Whether or not the Planet Nine theory is correct, the distant Planet Nine distributed over a wide range of ecliptic longitudes, IOCs and ETNOs are an important probe for studying the the most likely location of Planet Nine is close to the region origin and evolution of the very distant outer solar system and where the Galactic plane intersects the northern ecliptic (see testing alternatives to the Planet Nine theory (Morbidelli & Figure 6). The bulk of the predicted Planet Nine sky locations Levison 2004; Brasser et al. 2006, 2012; Gladman & are within the LSST footprint as implemented in the base- Chan 2006; Kaib et al. 2011; Zderic & Madigan 2020; line_v2.1_10yrs simulation, which includes the NES Emel’yanenko 2022; Huang et al. 2022). Observing across the minisurvey. Figure 7 presents the estimated on-sky V-band ecliptic will be crucial for creating a large enough sample to apparent magnitude distribution for Planet Nine from Brown alleviate the challenging observational biases currently dealt (2022) and the predicted LSST r-band limiting magnitudes from the baseline_v2.1_10yrs run. Over a wide range of with when combining the multiple data sets previously used possible V − r colors, Planet Nine could potentially be bright to identify and test the apparent orbital clustering 15 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. met: at least 18,000 deg with a median of 825 visits per field (Ivezić & the LSST Science Collaboration 2013). For extragalactic science, including cosmology and galaxy studies, and galactic science, such as the study of the Milky Way’s structure, there have been proposals from the community requesting more of the WFD to be shifted to low-extinction and less crowded sky (Lochner et al. 2018, 2022; Olsen et al. 2018). Other arguments have also been raised for shifting the WFD footprint further northward, including overlap with future DESI (Dark Energy Spectroscopic Instrument; Abareshi et al. 2022) and Nancy Grace Roman Space Telescope observations (Olsen et al. 2018; Capak et al. 2019b). As this is a zero-sum game, visits are taken from near the Galactic plane with high stellar crowding and dust extinction and redistributed north- ward above 0° decl. This results in a fraction of the WFD survey now covering the NES region as seen in the Baseline v2.0 and v2.1 footprints shown in Figure 2. The SCOC has made the recommendation to use this new footprint as shown in Figure 2 extending the WFD northward, although the final decl. limits of the WFD and the exact boundary of the Galactic/high dust extinction region can still be fine-tuned (Ivezić & the SCOC 2021). As implemented in baseline_v2.0_10yrs simulation, the revised WFD footprint has two decl. boundaries spanning from −72° to +12° decl. with an interstellar dust extinction cutoff at approximately E(B − V ) = 0.2 mag or A (V ) = 0.6 mag (Ivezić & the SCOC 2021; Yoachim 2022), where E(B − V ) is the dust reddening in magnitudes and A(V ) is the total V-band extinction. The northern boundary of the WFD varies with R.A. in this revised northward footprint; this is partly due to other additional constraints with the scheduler. We note that baseline_v2.1_10yrs simulation uses the same footprint as v2.0 but incorporates the Virgo Cluster (α = 12 hr, δ =+12°) into the WFD (Yoachim 2022). Figure 6. The simulated probability of Planet Nine (top) compared to the Expanding the WFD footprint northward will cover part of possible number of visits in the LSST footprint from the baseline_- v2.1_10yrs simulation (bottom). The Planet Nine probability density is the NES for “free” with the time charged to the WFD time taken from Brown (2022), which is based on 100,000 synthetic orbits and allocation, but part of the redistributed pointings in the 2°−12° physical properties of Planet Nine (including on-sky locations and V-band decl. band are at high ecliptic latitude because part of the apparent magnitudes) drawn from the distributions developed in Brown & ecliptic plane crosses the Galactic plane in the southern Batygin (2021), where we have removed the ones that are flagged as being ruled out by constraints from the ZTF (Brown & Batygin 2022) and the DES hemisphere. Transferring WFD visits from the Galactic bulge (Belyakov et al. 2022; Bernardinelli et al. 2022). The top panel has the LSST region will reduce the number of photometric data points footprint shaded by the number of observations that reach 5σ limiting available for generating rotational light curves for some MBAs magnitude of 24 in any filter. The most probable locations of Planet Nine are within the bulge, but how significant the impact is will depend within the NES region, but the full LSST footprint is required to search and probe the majority of the Brown & Batygin (2021) predicted Planet Nine on the exact shape of the footprint. The OCCs, NEOs, and parameter space. The plots are centered on α = 0 and δ = 0. R.A. and decl. PHAs are distributed across a wide range of ecliptic latitudes, lines are marked every 30°. so observations at higher ecliptic latitudes will still find small bodies in these populations. The same arguments that hold for (Brown & Batygin 2016, 2019; Shankman et al. 2017; outer solar system objects in Section 4.1.1 also apply in this Bernardinelli et al. 2020; Napier et al. 2021). case. Assuming a 2025 February 14 start date, Neptune’s on- sky position will have changed by about 1 hr in R.A. and 8° in 4.1.2. Extending the Wide–Fast–Deep Footprint Northward decl. by the end of LSST observations. Objects beyond 30 au will be moving slower than Neptune. Most of the TNOs and The NES minisurvey (as described in Section 4.1.1) was IOCs located in the NES at the start of the survey will remain in proposed when the northern limit of the WFD footprint was the NES throughout the duration of the LSST. As these distant initially set to be +2° decl. (see the baseline_retrofoot objects do not move very far on-sky during the 10 yr survey, simulation in Figure 2). The originally planned WFD sky any observations of the NES are beneficial for discovery as coverage used a simple cut in Galactic coordinates to identify long as not too much time is taken away from near ecliptic the boundary of the WFD with the Galactic plane/bulge pointings in the southern hemisphere. observing region (LSST Science Collaboration et al. 2017; In Figure 8, we evaluate the impact of the new northward Ivezić et al. 2019; Jones et al. 2020). Combining this boundary with the sky coverage requirements and visit constraints for the WFD sky coverage, comparing baseline_v2.0_10yrs WFD set the original northern decl. limit. What sky is included and baseline_retrofoot_v2.0_10yrs simulations. All within the WFD is a changeable LSST survey parameter, as simulations predating the v2.0 simulations start from a long as the SRD requirements for the WFD survey area are variation of the WFD with the old +2° decl. limit. The v2.0 16 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 7. The sky−magnitude parameter space for a simulated Planet Nine (bottom) compared to the possible LSST sky coverage and limiting magnitudes from the baseline_v2.1_10yrs simulation (top). The bottom left panel shows the median expected V-band apparent magnitude, and the bottom right panel has the maximum expected V-band magnitude from the distribution of Planet Nines, as described in Figure 6. Also shown are the LSST individual exposure median (top left) and 10 yr coadded (top right) r magnitude depths per pointing in the survey footprint. Since the optical color of the potential Planet Nine is not constrained, we do not apply any V − r color to allow for multiple comparisons depending on the reflectance model preferred by the reader. Observing the NES with Rubin Observatory is crucial for testing and constraining the Planet Nine parameter space. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. baseline also has additional changes to the observing cadence, 2.0/2.1 WFD footprint simulations that allow for a larger including tweaks to the rolling cadence implemented and the number of visits to be distributed across the sky. The v1.5 exposure time for u-band visits. For an apples-to-apples footprint family uses these additional visits to explore the comparison, the v2.0 release includes baseline_retro- impact of typically adding more northern visits in various foot_v2.0_10yrs, which uses the original v1.5–1.7 base- configurations, modifying the number of visits in the Galactic line WFD+NES footprint, leaving the other cadence plane and NES (sometimes in differing filters), and some parameters the same as the v2.0 simulation. The northward changes to the extent of the WFD footprint. The sky map WFD footprint produces a slight increase in discovery metrics showing the total numbers of visits per pointing in these v1.5 (all less than 5% change) for all populations except for the large simulations is shown in Figure 10. In general, adding visits Jupiter Trojans. There are also slight improvements in the light- northward enhances TNO discovery statistics, and in most curve metrics, with the smallest MBAs seeing more than a 10% cases, there are only small impacts on the ability to obtain light increase with the extended WFD footprint. These increases curves and produce shape inversion models of inner solar may be more significant than represented in the MAF metrics if system objects. Instead of adding a small number of visits, the stellar crowding was taken into account. Although this v1.7 WFD footprint experiments explore WFD variations comparison is to the v2.0 baseline, it will still hold true for on a dust-extinction-limited footprint with variable north/south the v2.1 baseline (baseline_v2.1_10yrs) that goes decl. limits. The total numbers of visits in these v1.7 WFD slightly more northward. We note that the addition of the footprint experiments are shown in Figure 11. Overall, Virgo Cluster is a minuscule change in area, and there are TNOs and outer solar system discoveries benefit the most, with negligible impacts to any of the solar system metrics compared the inner solar system object discoveries taking only a few to baseline_v2.0_10yrs (as discussed in Section 4.7.2). percent loss in discoveries. The light-curve metrics for the most For completeness, we briefly discuss the footprint experi- part see 5%–10% boosts in the various configurations of the ments performed in the v1.5 and v1.7 releases that led to the more dust-free WFD, but they start to decrease more revised northward WFD incorporated into the v2.0 and onward significantly for the smaller-sized MBAs, NEOs, and PHAs, LSST cadence simulations. The discovery and light-curve as less of the ecliptic that intersects with the Galactic plane in metrics are shown in Figures 4 and 9. The v1.5 footprint the southern hemisphere is included in the WFD and the simulations are set up with different overheads than the v1.7/ number of visits to those regions drops. Some caution needs to 17 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 8. Impact of the revised v2.0 LSST footprint with the northward and dust-extinction-limited WFD. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. be taken in interpreting this result, as this loss may be less than of detections in the simulations, this is likely contributing to the what is shown by the metrics as detection efficiency of solar variation observed in the light-curve metrics. Overall, these system objects (and by extension the ability to measure their v1.5 and 1.7 footprint experiments show that moving visits light curves) decreases in crowded fields. The Jupiter Trojans northward is an improvement and paved the way for the are constrained in set locations on the sky; with small numbers optimized v2.0/v2.1 WFD footprint and full LSST footprint. 18 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 9. Possible tuning options for the WFD survey footprint from the v1.7 experiments. As visits are taken away from the Galactic plane and bulge region, they are redistributed northward and southward to less dust extinction and less stellar crowded regions. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. 4.1.3. Varying the Fraction of Time Spent in the Non-WFD Regions discussed below show how covering these specific areas with visits ranging from 1% to 100% of the WFD cadence affect Three sets of simulations were done in which the visits to the metrics for solar system populations. NES and the Galactic plane were varied relative to the WFD The vary_NES family of simulations included coverage of the coverage. In these simulations, varying numbers of extra visits fields in the NES at 1% of the WFD level, at 5%–55% of the to the NES or the Galactic plane are added at the expense of removing that observing time from the WFD. The simulations WFD level in 5% increments, and at 75% and 100% of the WFD 19 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 10. The total number of visits in all filters after 10 yr for the v1.5 footprint experiment simulations. The DDFs are also visible as a collection of single fields receiving a higher number of observations than a WFD pointing. Each DDF receives approximately 1% of the total LSST observing time. The filterdist_indx2_v1.5_10yrs run does not include DDFs. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. level; the baseline simulation has the NES fields at 30% of the coverage levels for the NES (<10%), the discovery metrics for the WFD. The top panel of Figure 12 shows the discovery metrics for TNO populations are reduced by more than 5% relative to various solar system populations for these simulations. At low baseline, and most populations show increasing discoveries as the 20 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 11. The total number of visits in all filters after 10 yr for the v1.7 WFD survey footprint exploration simulations. As visits are taken away from the Galactic plane and bulge region, they are redistributed northward to pointings with less stellar crowding and dust extinction. The DDFs are also visible as a collection of single fields receiving a higher number of observations than a WFD pointing, with each DDF receiving approximately 1% of the total LSST observing time. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. NES coverage increases. Figure 13 shows the fraction of each (down to H = 6) that are observed in at least four filters is solar system population (relative to baseline) that is observed in at reduced by more than 20% compared to baseline; the NES must least fourofthe grizy filters as a function of the NES coverage. If cover at least 25% of the WFD to not reduce this metric by more the NES is covered at 15% of the WFD, the fraction of TNOs than 5%. The TNO populations are the most affected in both 21 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 12. Varying the time spent on the NES. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. Note: the light-curve inversion plot has been truncated for clarity. The MBAs and Jupiter Trojans extend beyond the plot for the 1.0 NES fraction. discovery and color light-curve metrics when the NES is not faint MBAs and faint Jupiter Trojans that are expected to have covered to at least 25% of the WFD level because they move light-curve measurements (bottom panel of Figure 12); all the slowly on-sky compared to closer-in solar system populations. populations generally improve in both the color light-curve and Most of them will not move enough over the 10 yr LSST time light-curve metrics as NES coverage increases. span to move from NES fields to WFD fields. Covering the NES The vary_GP family of simulations included coverage of at <25% of the WFD also significantly decreases the number of the fields in the Galactic plane at 1% of the WFD level, at 5%– 22 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 13. Color light-curve metrics with varying time spent on the NES from the v2.0 simulations. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. 55% of the WFD level in 5% increments, and at 75% and 100% curve metrics (see Figure 16) are also generally similar across of the WFD level. Figure 14 shows the resulting solar system the family of simulations, with losses in the fraction of faint metrics for discovery and light-curve inversion. The discovery populations observed in four of the grizy filters that hover metrics for different solar system populations are all within 5% around 5% for the simulations with higher threshold values of baseline for these simulations, and the fraction of each (above ∼0.4), with or without pencil beams; the simulations population that has observations in multiple filters is also with the lowest thresholds show an enhancement in the color relatively unaffected. However, when the Galactic plane is light-curve metric. However, for this entire family of simula- covered at >30% of the WFD level, the fractions of faint tions, the fractions of faint MBAs, Jupiter Trojans, NEOs, and MBAs, Jupiter Trojans, and PHAs with light-curve inversions PHAs with light-curve inversions all suffer >5% losses all drop by 5% or more (increasing losses with increasing compared to the baseline simulation (bottom panel of Galactic plane coverage) compared to the baseline simulations. Figure 15); again, this is likely due to additional time shifted This is likely simply a result of shifting time away from the away from the WFD fields. The set of simulations that cover WFD fields, decreasing the odds that the fainter solar system the priority map at >0.6–1.2 threshold with or without pencil objects are above detection thresholds multiple times in the beams generally keep the light-curve inversion losses for these reduced number of visits to their fields. populations to between 10% and 20% compared to baseline. The plane_priority family of simulations varies how The simulations with four larger or 20 smaller Galactic plane different regions of the Galactic plane are covered based on pencil beam fields added in addition to the plane priority maps a priority map of the Galactic plane from the Rubin have worse light-curve inversion metrics for all solar system Observatory LSST Stars, Milky Way, and Local Volume and populations than simulations with just the priority maps. Transients and Variable Stars science collaborations. Some of these simulations also have pencil beam fields in areas of the Galactic plane that the WFD is not planned to cover. These targeted pencil beam fields would be visited at the same level 4.2. Exposure Times and Snaps as the WFD. The Galactic plane plane_priority simula- In this section we explore the various options for the total tions were completed with and without pencil beam fields, and exposure time per visit and the number of observations two additional simulations were done with just four larger or 20 (“snaps”) taken at each visit. Both parameters directly impact smaller Galactic plane pencil beam fields (the pencil_fs the amount of open shutter time available and therefore how simulations). The discovery metrics for different solar system many exposures can be taken on any given night and in total by populations are almost all within 5% of baseline for these the survey per filter. The visit exposure time also impacts the simulations, with only faint MBAs and faint Jupiter Trojans individual image depth, increasing or decreasing the resulting dropping slightly below those thresholds for the priority image’s5σ limiting magnitude. threshold at 0.1–0.2 (top panel of Figure 15). The color light- 23 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 14. Varying the time spent on the Galactic plane (v2.0 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. 4.2.1. Snaps Section 4.2.2). The original plan was for the Rubin The LSST cadence is currently planned with two Observatory data management pipelines to compare the two snaps in order to identify and flag pixel-level artifacts exposures of equal length dubbed “snaps,” nominally 15 s each, to be taken back-to-back at each visit to an on-sky (e.g., cosmic rays). Source detection would be performed on pointing, except in the case of u-band observing (see the image resulting from coadding the two exposures 24 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 15. Varying the time spent on the Galactic plane (v2.1 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. (Ivezić & the LSST Science Collaboration 2013;Ivezić et al. There now exist many algorithms published in the literature for 2019). We note that the Rubin SSP pipelines’ discovery identifying cosmic rays in single astronomical images (e.g., algorithm is agnostic to the number of snaps per visit, as it Rhoads 2000; van Dokkum 2001;Shamir 2005; McCully et al. uses the transient sources detected in the coadded snaps 2018). If these algorithms work well on LSSTCam images, there image as input (Myers et al. 2013;Jurić et al. 2020). may be no strong reason for taking two snaps at each visit. The 25 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 16. Color light-curve metrics with varying time spent on the Galactic plane (v2.1 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Table 4 Varying Snaps Exposures LSST Cadence Total No. Area with per Visit Simulation Name of On-sky >825 Visits Visits (deg ) 2 × 15 s (current SRD requirement) baseline_nexp2_v1.7_10yrs 2,045,493 17,982.71 1 × 30 s baseline_nexp1_v1.7_10yrs 2,208,619 18,190.85 decision on whether to implement one snap or two snaps per visit enabling other science. In the v1.7 simulations, the extra visits will be made during commissioning of the LSSTCam and the gained in the one snap case were divided out evenly between the Rubin data management pipelines (Ivezić et al. 2019) when the WFD and other parts of the simulation’s survey footprint. This feasibility of single exposure cosmic ray rejection can be tested produces an increase in both the detection and the light-curve and the impact from satellite constellation streaks can be properly metrics (see Figure 17). The detection metrics for the small size assessed (see Section 5.4). With two snaps, each planned visit has end increase by a few percent. The extra visits provide additional two camera readouts and two camera shutter openings and chances for those objects near the survey brightness limit to get closings. Although the readout time and the movement of the above the image 5σ limiting magnitude and be detected. The camera shutter are relatively quick (well less than a minute),the largest bodies see only a very slight increase because the majority summed time lost to these overheads over the entire 10 yr survey of times when they land within an exposure they are already is nonnegligible in the case of the two snaps observing cadence. brighter than the limiting magnitude. The largest enhancement is As can be seen in Table 4 for the v1.7 family of simulations (the seen with the light-curve inversion metrics, especially for the most recent LSST cadence simulations exploring the number of small end of the size distribution, where we find a >20% boost snaps), switching to one snap generates an ∼8% gain in on-sky across the MBAs, PHAs, NEOs, and Jupiter Trojans. Like the visits and an increase in the sky coverage reaching the WFD goal case for discovery, the extra observations provide more of 825 visits per pointing. opportunities for better temporal coverage to probe rotations and The impact of switching to a single-snap cadence will depend perform shape inversion. Since only six detections are required for on what the added exposures are used for. The SCOC has not yet discovery, it is the light-curve inversion metric that shows the true made any decision about snaps and how to partition out the extra benefits for color and light-curve measurements from the extra on- visits. If a significant portion of the gained visits can be distributed sky observing. across the entire LSST footprint or WFD and NES, the increase in As noted in Schwamb et al. (2018b), there is some extra exposures will add sky coverage and/or temporal coverage that information that is potentially gained with two snaps per visit. will help with detection and monitoring of small bodies while Although the SSP pipelines are not currently planned to use 26 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 17. Varying the number of snaps per visit (v1.7 simulations). The case for the reference simulation with two snaps is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light- curve inversion metrics. any information from the individual snaps, bespoke community and (2) brightness variations (on the order of seconds) to be software could be developed to take advantage of the two extracted from the streaks for ultrafast rotators. Only a very tiny exposures per visit. For those small body populations moving fraction of the asteroids discovered will be rotating fast enough fast enough to be significantly trailed in the LSST images, such that sub-30 s resolution will be useful (Pravec & Harris 2000; as NEOs and PHAs, the sequential snaps allow for (1) the on- Masiero et al. 2009; Warner et al. 2009, 2021; Hergenrother & sky direction of motion to be measured from the two streaks Whiteley 2011; Chang et al. 2014, 2019, 2021, 2022). These 27 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. are very limited benefits compared to the gains to all solar discovery metrics is greater for fainter PHA and NEOs with system populations from an extra 8% of on-sky observing time. 16 < H < 22 than for bodies with H < 16. The enhanced Therefore, we recommend incorporating single-snap visits into discovery caused by the greater coverage is due to the fact that the LSST cadence, if feasible. these closer-in objects tend to be detected at viewing geometries when they are closer to Earth and are thus brighter to compensate for their smaller size. Examples of faint, close-in asteroids whose 4.2.2. Long u-band Observations discoveries are favored by the shorter cadence when they The long_uX and u_long simulations explore the impact approach Earth include asteroids on Earth-similar orbits and of using a single longer u-band exposure versus two shorter (15 meteoroids (e.g., Kwiatkowski et al. 2009;Granvik et al. 2012; s) exposures in the baseline. This was investigated since, Bolin et al. 2014, 2020a; Jedicke et al. 2018;Shoberetal. 2019; because of the low level of sky background in u band, readout de la Fuente Marcos & de la Fuente Marcos 2020b, 2022; noise has a larger impact than in redder bands, and a single Fedorets et al. 2020b; Naidu et al. 2021). longer exposure allows us to significantly improve the u-band When exposure times are increased to more than 30 s, more depth (see, e.g., Jones et al. 2020). The u_long family varies distant objects have improved discoveries, but the discovery of the duration of the single u-band exposure (30, 40, 50, or 60 s). PHAs and NEOs diminishes. The decreased discovery of PHAs It is expected that longer u-band exposures (40 or 50 s) will be could be due to the decreased sky coverage in the longer-exposure advantageous for the detection of faint activity around solar scheme compared to the shorter-exposure scheme. The degrada- system objects. Since the u band also encompasses emission tion in the number of PHAs and NEOs in the longer exposures from the CN radical around 388 nm (and to a lesser extent from could also be due to trailing losses from their higher rate of motion the NH radical), there might be slight gains for active comets (e.g., Shao et al. 2014;Yeetal. 2019). One additional factor to inside 3 au, increasing with decreasing heliocentric distances, consider in the longer exposure times is that they will be more but this has not been modeled in detail yet. However, longer u- susceptible to images being compromised from satellite trails, band exposure times (starting marginally with 50 s but more which is more likely in longer exposures (Tyson et al. 2020);see strongly for 60 s) result in a lower number of observations Section 5.4 for a detailed discussion. being performed in other filters and thus decrease the number The effect of shortening the exposure time improves the light- of faint Jupiter Trojans and PHAs detected, as well as the curve inversion metrics for all dynamical groups of objects number of faint objects for which we can perform light-curve included in the v2.1 cadence simulations as seen in the bottom inversion, as illustrated in Figure 18. The long_uX uses a 50 s panel of Figure 20. Shorter exposure times generally improve the exposure, either keeping the same number of visits (long_u1) light-curve inversion metrics owing to the improved coverage and or reducing it (long_u2). Both of these tend to be worse than improved density of detections enabledbyshorter exposure the baseline for solar system objects in terms of light-curve cadences. The magnitude of improvement varies by dynamical inversion in particular for faint objects, as illustrated in class. For faint PHAs and NEOs with H = 19, the light-curve Figures 19 and 18. This results from the fact that light-curve metric is almost doubled, with 20 and 22 s exposures compared to inversion requires a certain number of observations above a the baseline cadence. Larger PHAs and NEOs with H = 16 see a certain S/N threshold, which might not be met for some objects moderate improvement as well with the shorter exposures. The in bluer filters, where most solar system bodies are fainter. The higher density of coverage will also be useful for the study of the long_u2 family performs better for both detection and light- rotation states of Jupiter Trojans and asteroid family members in curve inversion metrics and was identified as a good the main belt (Hanuš 2018), e.g., as shown by the increase in the compromise as long as it is not done together with any of the light-curve metrics for Trojans and MBAs. The benefits of wider bluer_indxXX options mentioned in Section 4.3, which is and more frequent coverage of the sky to light-curve inversion shifting more visits to blue filters over redder filters. may also extend to the monitoring and detection of activity within the asteroid belt (e.g., Moreno et al. 2017). The improvement for 4.2.3. Other Variations of Exposure Times more close-in objects may be explained by their higher sky-plane motion, placing this in a wider range of possible areas of sky The visits in the v… 2.0 survey simulations are typically set to positions that is more easily covered with a shorter cadence. A 2 × 15 s exposures in the grizy filters, while the u band has good compromise exposure time for obtaining favorable 1 × 30 s exposures. A series of simulations (v2.1 shave) has discoveries for inner and outer solar system objects, as well as been run to explore the impact of different exposure times on the dense light curves, seems to be the 30 s exposure cadence. survey metrics compared to the family’s baseline simulation. As An additional simulation, vary_expt_v2.0_10yrs, was seen in the top panel of Figure 20, the relative effect on the designed to test the results of varying the exposure times discovery rate of TNOs, faint OCCs, and faint MBAs diminishes between 20 and 100 s in the ugrizy filters to provide significantly with shorter exposure times compared to the baseline consistency in the image depth in different filters. As seen in exposure time configuration, as the 5σ limiting magnitude the top panel of Figure 19, varying the exposure time between decreases with exposure time. Shorter exposure times have a 20 and 100 s results in poorer discovery metrics relative to the greater effect on fainter absolute magnitude TNOs, dropping the baseline simulation for all classes of solar system objects used discovery metrics by more than ∼5% for TNOs with 6 < H < 8 in the simulations. This is due to the fact that the longer compared to TNOs with H < 6. The effect is similar for OCCs, exposures result in an overall decrease in survey coverage and a with the discovery metrics decreasing by ∼5% for OCCs with decreased chance to detect moving objects. As seen in the 8 < H < 12 compared to OCCs with H < 8. The discovery of NEOs and PHAs does see a small improvement with the shorter bottom panel of Figure 19, the effect on light-curve inversion exposure cadences owing to increased sky coverage resulting metric is also worse for all classes of solar system objects. from the shorter exposure times allowing for more exposures to be Therefore, varying the exposure times to achieve uniform visit taken (e.g., Jedicke et al. 2016). This improvement in the depth is not recommended. 28 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 18. Changing the length of the u-band exposures in the v1.7 simulations. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. example), or modifying how observations in different filters are 4.3. Filter Cadence and Filter Distribution interspersed within a night or throughout a lunation. First, we This section explores decisions focused around the choice of examine the effects of increasing the number of observations in u filter, i.e., changing the distribution of observations across filters and g bands compared to the baseline. Next, we explore the consequence of imposing that a certain number of observations (to increase the total number of observations in bluer filters, for 29 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 19. The impact of skewing the filter distribution bluer, increasing the exposure time of the u-band observations, and the effect of varying exposure time per visit (vary_expt_v2.0_10yrs). All the simulations presented in this figure are from the v2.0 runs. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. are performed in various combinations of filters each year. Lastly, The bluer_indxXX family of simulations have a bluer filter we investigate changing the cadence of observations in g band, distribution, increasing the number of exposures in g,or u and g taking advantage of bright time to schedule extra visits and reduce filters compared to the baseline (the filter balance in the baseline is the gap between successive observations of a given field in “u”: 0.07; “g”:0.09; “r”:0.22; “i”:0.22; “z”:0.20; “y”:0.20).This g band. is done by removing visits in redder filters to redistribute them 30 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 20. Variations on the effective exposure time per visit in the v2.1 cadence simulations. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Except for the baseline simulation, which has 2 × 15 s visits in grizy and 1 × 30 s in u, all other simulations in this run had single exposure visits per pointing. Top: discovery metrics. Bottom: light-curve inversion metrics. between u and g. Similarly to the families discussed above, from CN and C radicals, respectively. As discussed above and increasing the number of exposures in u or g band results in a illustrated in Figure 21,a u-heavy distribution induces a severe decrease of the number of faint objects for which light- significant decrease in the detection of faint solar system objects curve inversion is possible. Even though this has not been that are fainter in u band, as illustrated in Table 1. modeled yet, active objects close to the Sun might benefitfrom Figure 22 presents a set of simulations where emphasis is put increased u and g coverage, as these filter encompass emissions on obtaining a handful of exposures with a seeing <0 8 each 31 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. year, varying the weight put on that constraint and the at which SSP can detect moving sources. The time between combinations of filters for which it has to be met (whether i repeat visits also directly impacts the number of pairs observed and y are included or not). These simulations come as a request per night and thus the total area searchable for solar system for extragalactic science cases. Ensuring that there are yearly objects. We aim to find the best pair time separation that good seeing images in several filters enhances strong lensing increases the distances that SSP is sensitive to without making detection (Verma et al. 2019) and galaxy studies (Ferguson a significant trade-off in observing efficiency. This would allow et al. 2021). In general, requirements of having a minimum the Rubin SSP to detect more distant TNOs while not number of good seeing images per year in various bandpasses compromising the discovery and characterization of the more do not impact strongly our discovery or light-curve metrics inward solar system populations. We note that the tunable (except for the good_seeing_gsw1.0_v2.1_10yrs and parameter here is the Rubin scheduler’s goal for spacing the good_seeing_u_gsw0.0_v2.1_10yrs). repeat visits in a given night. In reality, this will be a The cadence_drive family of simulations investigates distribution centered about the ideal value the Rubin scheduler reducing long gaps between g-band visits over a month by is aiming for. This is shown in Figure 24 for three examples requiring a certain number of fill-in visits each night during from the v1.7 pair_times simulations that take mixed filters bright time. Adding g-band visits during full moon time (and with ideal separations between 11, 22, 33, 44, and 55 minutes. consequently reducing the number of visits in redder bands) is Solar system objects appear to move fastest on-sky when generally detrimental for solar system objects, and in particular they are at opposition, where the apparent motion is dominated for light-curve inversion of faint Jupiter Trojans as illustrated in by the parallax induced by Earth’s movement. The on-sky rate Figure 23. A small number (30) of contiguous visits might be of motion at opposition for a body exterior to Earth’s orbit on a acceptable, but in general the lowest possible number of g-band circular and coplanar orbit can be defined as fill-in visits is preferable. -0.5 1 - r dq ⎛ h ⎞ = 148 ,3() ⎜⎟ 4.4. Visits within a Night dt r - 1 ⎝ ⎠ The Rubin SSP pipelines will search nightly image pairs for where r is the body’s heliocentric distance in astronomical new moving sources. Once the orbit of a solar system object is dq units and is the apparent motion at opposition in arcseconds known sufficiently well, SSP will be able to predict the orbit dt and identify previously known small bodies in single LSST per hour (Luu & Jewitt 1988). We assume 140 mas as a observations, but throughout the entire 10 yr new solar system conservative estimate for the astrometric uncertainty for discoveries will be made (Myers et al. 2013; Jurić et al. 2020). sources near the LSST detection limit and a 3σ positional shift The majority of the TNOs and MBAs will be picked up within for the SSP pipelines to successfully identify the moving object the first 2 yr of the survey, but new comets, NEOs, and ISOs as a new source in the second observation (M. Jurić 2022, will continue to be discovered across the duration of the LSST private communication). This translates to solar system bodies (Eggl et al. 2019). Thus, it is important that nightly pairs be having to move at least 0 5 between the visits in order to taken over the full span of the LSST. become detectable by SSP, setting a minimum speed limit. In The LSST SRD (Ivezić & the LSST Science Collabora- Figure 25, we estimate SSP’s motion limit for the range of pair tion 2013) requires at least two observations per night at each separations, including those explored in the pair_times observed pointing in order to facilitate accurate removal of the simulations. The solid line represents the opposition on-sky solar system “cruft” that will pollute the millions of transient astrophysical LSST alerts sent out. A transient only seen in one rate of motion as calculated from Equation (3). but not in a repeat observation on the same night will most The bulk of the classical Kuiper Belt extends from ∼42 to likely be due to a previously undiscovered moving small body. 47.7 au, the 2:1 MMR with Neptune, but the Kuiper Belt’s Multiple observations in the same night also help differentiate scattered/scattering disk and detached/high-perihelion TNO inner solar system objects from outer solar system bodies. population (with perihelion at ∼50–80 au) do extend well Additionally, these repeat visits provide temporal and color beyond that (Trujillo & Brown 2001; Petit et al. 2011; Adams information that can be used to probe the evolution of et al. 2014; Bannister et al. 2018; Bernardinelli et al. 2022). astrophysical transients (e.g., Bianco et al. 2019; Setzer Separations longer than 18 minutes are needed to search for et al. 2019; Andreoni et al. 2022; Li et al. 2022; Lochner objects beyond 80 au. Separations longer than 33 minutes start et al. 2022) and minor planets. Below we explore several to slightly negatively affect the discovery metrics and proposed options on the number, time separation, and filter significantly enhance the light-curve metrics, as plotted in choices of these intranight visits. Figure 26. The loss of discovery at fainter absolute magnitudes is less than 5% even at 55-minute spacings. As the time gap gets longer, the pairs are more vulnerable to interruptions, 4.4.1. Separation between Nightly Pairs mostly from weather. The fraction of gri pairs peaks at 22 Nightly pairs in combinations of the g, r, and i filters are the minutes, but the total visits and the on-sky area reaching 850 most conducive to finding solar system objects. We explore the visits both increase with longer pair separations. The light- intranight separations in these combinations of filters. As curve inversion metrics go up with longer gaps between discussed in Section 2.3.4, the SSP pipelines require that intranight visits, due to the increase in the total number of motion be seen between the two exposures (Myers et al. 2013; visits, with a larger number of singleton images that are spread Jurić et al. 2020). If a solar system body has not moved out across the observable sky (see Table 5). Having the Rubin sufficiently for it to be identified as a new transient source in scheduler aim for the two visits to be separated by 33 minutes the next visit, SSP will not be able to spot that moving object. is the best compromise between optimizing the number of The separation between nightly pairs sets the farthest distance nightly pairs completed and heliocentric distance probed. We 32 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 21. Additional options for tuning the filter distribution (v1.5 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. note that the SCOC moved the LSST baseline strategy from various combinations. These simulations begin the extended aiming for 22-minute nightly pair separations to 33-minute pair separation either in Year 1 (delayed-1) or after Year 5 ones from the v2.0 simulations onward (Ivezić & the (delayed1827). This simulation family explores the impact SCOC 2021). of executing the long gaps strategy every night and less The v2.0 long_gaps_np (long gaps, no pairs) simulations frequently, where the nightsoff parameter (the number of extend the time between nightly repeat exposures to 2–7hrin sequential nights with no long gap sequences) is varied. On the 33 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 22. The impact of adding a requirement for three “good seeing” (seeing < 0 8) images per year in various bandpasses (v2.0 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The baseline_v2.1_10yrs includes the good seeing requirement for r and i bands as the default. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. nights when the long gap observing is not active, the scheduler for addressing the temporal coverage of fast transients are aims for 33-minute nightly pair simulations like the v2.0 explored in Section 4.4.4. As seen in Table 5, the fraction of gri baseline survey. These simulations are one option explored to pairs is largest when the Rubin scheduler is tasked with 33- potentially better capture fast-evolving astrophysical phenom- minute pair spacings. Therefore, these hours-long separations ena, as suggested by Bellm et al. (2022); additional strategies are not going to be efficient in generating nightly pairs 34 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 23. Investigating ways of reducing the gaps between g-band visits over a month (v1.7 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. conducive for the moving object search. This can be seen in the in intranight visits occurring every night have less impact. The discovery and light-curve metrics displayed in Figure 27. less time devoted to the large time gap pair observing, the less Across all populations, the light-curve metrics and detections severe the hit to the discovery and light-curve metrics. decrease. The increased sensitivity to objects beyond 150 au is Nonetheless, 33-minute pair separations are better optimized not worth the trade-off purely from a planetary astronomy for outer solar system discoveries and the completion of repeat perspective, but the simulations that do not have the long gaps visits within the night. 35 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 24. Distribution of nightly pair separations across the WFD and the NES from three simulations that make up the v1.7 pair_times family. The histograms are truncated at 120 minutes. for, and having same-filter nightly pairs reduces the number of different nights (and thus different longitudes) an object might be observed in that filter; for faint Jupiter Trojans, this appears to reduce the odds that successful detections in a given filter span the required range of longitudes. 4.4.3. Suppressing Extra Visits In the baseline cadence (baseline_v2.1_10yrs),upto 20% of the pointings are visited more than twice per night. By adding an additional basis function to suppress these repeat visits to the Rubin scheduler algorithm, the additional visits can be distributed to different nights, thus changing the internight cadence or season length for a given field. The suppress repeats (no_repeat_rpw) family of simulations (as shown in Figure 30) explores these changes by considering six different values for the weight of the suppression factor, indicated as rpw, namely 1, 2, 5, 10, 20, and 100. This number basically Figure 25. The opposition on-sky motion observed on Earth as a function of different heliocentric distance (solid line). The colored points represent the reflects how strongly the suppress-revisits basis function calculated slowest motion/distance detectable by the Rubin SSP pipeline for a influences the scheduler: the higher the number, the lower the range of nightly pair spacings. number of revisits per night will be. Note that some regions of the sky will still be observed more than two times within a 4.4.2. Filter Choices for Repeat Visits in a Night night if they are included in overlapping pointings. An immediate consequence of redistributing the visits over In the v1.5 simulations, cases were run with nightly pairs of different nights is a decreased total area with more than 825 visits performed in either matching filters (baseline_same- visits per pointings, from a negligible effect (0.1% at filt_v1.5_10yrs) or in mixed filters (baseline_- rpw = 1) to a more significant effect of 5% at rpw = 20. v1.5_10yrs). The discovery metrics for solar system However, because of the extended timeline, the discovery populations are largely unaffected by the choice (top panel of metrics are generally improved with respect to the WFD: a Figure 28). This is because the mixed-filter pairs in the cadence suppressing factor between 2 and 10 will increase the discovery simulations contain filter pairs such as g− r and r− i,where the rate for all the different families, while for rpw = 1, 20, or 100, colors of solar system objects allow detections in both filters (we there is only a marginal decrease (0.005%) in the discovery note that r− i pairings are better than g− r pairings for the rate of faint TNOs and bright comets from the Oort Cloud. A reddest objects like TNOs). Similarly to what is shown in suppressing factor equal to or larger than 10 will also impact Figure 29, the color light-curve metrics for different populations the metrics of light-curve inversion, reducing up to ≈10% the are not significantly affected by the choice of same or mixed number of faint MBAs and Jupiter Trojans for which inversion filters; there is likely an advantage to having mixed filters within will be feasible. Summarizing, the “suppress visit” family the same night in that it could provide a single-night color cadence produces negligible effects on solar system science, estimate for objects that rotate slowly compared to the visit with a marginal improvement on the discovery rates for separation. For faint NEOs and MBAs, nightly pairs in the same rpw = 2 and 5 and a marginal decrement of the number of filter do boost the light-curve inversion metric by 15%–20% faint objects for which we will be able to perform light-curve (bottom panel of Figure 28). This is likely due to nightly pairs of inversion for rpw = 10, 20, or 100. faint objects that are only detectable in a small number of filters. However, faint Jupiter Trojans suffer a 30% loss in the light-curve 4.4.4. Third Visits in a Night metric for the same-filter pairs. This is likely related to the light- curve metric requirement that observations in a filter span at least There is a strong desire among other Rubin Observatory 90° in ecliptic longitude. The Jupiter Trojans move more slowly LSST Science Collaborations to add a third visit in a different on-sky compared to the other populations this metric is calculated filter to aid in capturing and identifying fast (<1 day) transients 36 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 26. Changing the ideal nightly pair separation (v1.7 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. by adding more color information (see Bianco et al. 2019) to presto_half simulations explore the effect of adding the the base survey of nightly pairs of visits that are separated by third image/triplet every other night rather than every night of ∼20–30 minutes. The presto_gap family of simulations the cadence, while the presto_gap_mix has a wider explores the effects of adding a third visit to the night’s visits separation and difference in colors between the initial pair after a time period of 1.5–4 hr. Within the presto family of and the third visit (e.g., g + i, r + z, i + y rather than g + r, r + simulations, there are two significant subfamilies. The i, i + z initial pairs). 37 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 5 Diagnostics for the LSST Cadence Simulations Changing the Desired Separation between Nightly Pairs LSST Cadence Simulation Name Ideal Separation Total No. of Area with Mean Fraction (rms) between of On-sky > 825 Visits of WFD+NES Visits Nightly Pairs Visits (deg ) in 15-to-60-minute (minutes) Separated Pairs g, r,or i Filters Only pair_times_11_v1.7_10yrs 11 1,947,985 14,356.96 0.240 (0.061) baseline_nexp2_v1.7_10yrs/pair_times_22_v1.7_10yrs 22 2,045,493 17,982.71 0.586 (0.055) pair_times_33_v1.7_10yrs 33 2,075,493 18,076.71 0.546 (0.057) pair_times_44_v1.7_10yrs 44 2,089,977 18,104.40 0.475 (0.061) pair_times_55_v1.7_10yrs 55 2,100,189 18,108.60 0.398 (0.061) Notes. These simulations used 2 × 15 s snaps per visit. 15-minute separations cover the full classical Kuiper Belt; 60 minutes was chosen as the upper limit because the bulk of the nightly pairs in these runs are separated by less than this value (see Figure 24). The main impact of adding this third visit is to dramatically longer gaps for the potential third visit in the night, could decrease the amount of well-covered survey area (see constitute a path toward satisfying the desires of other science Figure 31). This would have a large negative impact on goals without unduly compromising solar system science. The science cases where the objects are sparse on the sky such as impact of the loss of sky area covered in all of these third visit discovering rare objects (e.g., ISOs) or the onset of activity on simulations on the detectability of rare but high-value targets that are sparse on the sky, such as ISOs or very distant extreme solar system objects. The other large effect of adding the third TNOs (ETNOs and IOCs), needs additional simulations with visit is seen in the solar system object detection and light-curve these populations added. The addition of the OCCs to the later inversion metrics and illustrated in Figure 32. Although there is versions of the simulations, which are much more numerous on some improvement in the detection of the brighter solar system the sky than either ISOs or Sedna-like objects, and the objects at the shorter gap lengths in the 1.5–2.0 hr regime (see, corresponding drop in OCC discovery when adding the third e.g., the presto_gap1.5_mix simulation in Figure 32), this visit show the downside of adding the third visit on discovering is not a high priority for the large-aperture capabilities of Rubin the rarer solar system populations. Observatory. For the vast majority of the other simulations and solar system populations, this family of simulations produces a 20%–75% decrease in the light-curve inversion metrics, well 4.5. Rolling Cadence beyond our threshold for flagging these simulation families as bad for solar system science. The impacts are less dramatic for Spreading the 825 observations of each field in the WFD the presto_half subfamily, as might be expected, since the evenly over the periods that they are observable, over 10 yr, third visit is only carried out 50% of the time. The impact of the corresponds to an observation of each field every three to four _mix version of a simulation (with the wider spread of nights, on average. As this is a relatively low cadence for some observed colors in the third visit) is always worse than the science topics (notably transients), a proposed pattern of corresponding “nonmixed” simulation run. observations increases the frequency in certain areas of the sky As an alternative to the presto families discussed above, in some years, at the cost of a lower cadence elsewhere, and the long_gaps_nightsoffN family (not to be confused then reverses the pattern the following year. This is referred to with the long_gaps_np family of simulations considered in as a rolling cadence. There are a variety of flavors of this Section 4.4.1) also adds a third visit in the same filter as one of approach, depending on how many stripes each half of the sky the pairs (like the presto family). However, unlike the (north/south) is divided into and the “strength” of the rolling, presto families, (1) the third visit forming the triplet is in one i.e., the fraction of the time spent in the “on” stripes compared of the same filters as the earlier pair, (2) it only occurs if the to the “off” ones (see Figure 33 for an illustration of these first pair is in the griz filters, and (3) it occurs after a longer 2–7 patterns). No rolling cadence entirely neglects the “off” stripes, hr gap from the initial pair than the standard ∼33-minute gap. but in some cases these areas see only a few observations in the This is done every N nights (N = 0K7), for example, entire year, to support template building. Over the 10 yr of the long_gaps_nightsoff7 has the long gaps every seven survey, the pattern of on/off stripes balances out to give nights, and long_gaps_nightsoff0 has “zero nights off” uniform coverage across the whole WFD area. For the majority and the long gap third visit/triplets are done every night. These of simulations rolling cadence is only applied to the WFD area families additionally come in two flavors, delayed-1 and (not the bulge, NES, or other “extended” survey areas) and is delayed1827, where the third visit/triplets start either not used in the first and last 1.5 yr of the survey. Video immediately before the start of the survey (night −1) or in animations of three example rolling cadence scenarios are survey year 5 (night 1827), respectively. available (via the online version of the paper) in Figures 34–36. Overall this family of simulations has much smaller The effect of rolling cadence is generally seen as positive for detrimental effects (<10%) on the area covered (final third of most science cases, for example, having a denser coverage of Figure 31) and most solar system metrics, except for where this light curves in the “on” stripes enhances transient science, and is done every or almost every night (the _nightsoff0 and rolling cadence is included in the baseline v2.0 simulations. _nightsoff1 simulations), which hit the area and light- However, there are positive and negative effects that vary with curve inversion metrics hard (20%–60%; see final third of the pattern and strength of the rolling cadence. There is little Figure 32). These families of survey strategy simulations, with difference between patterns that split the WFD into two or three 38 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 27. Impact of various scenarios for lengthening the gap between pairs to be variable in the range of 2–7hr (v2.0 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. The y-axis is truncated in the light-curve inversion plot. The Jupiter Trojans extend below the y-axis range for long_gaps_np_nightsoff0_delayed-1_v2.0_10yrs. The baseline simulation has an ideal separation of 33 minutes. stripes north and south of Cerro Pachón, but a more extreme Figure 37 is for faint Jupiter Trojans, which is not surprising, as six-stripe pattern, especially at high rolling strength, has more the Trojan clouds have a limited spatial extent that is in an significant effects on both discovery and light-curve metrics approximately fixed direction in a given season, relative to (Figure 37). Such a pattern is also vulnerable to extended where the corresponding planet is. As the cloud may fall into periods of bad weather in one season, resulting in uneven final either an on or off stripe in a given season, Jupiter Trojans coadded survey depth, so it is not favored for many areas of experience feast or famine in terms of observations, which may LSST science. The largest variability in the metrics shown in not even out over the years in the same way that more distant 39 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 28. Nightly pairs in the same vs. different filters (v1.5 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. populations (TNOs) will—this will depend on the precise potentially affect follow-up of rare objects like ISOs, or impact timing and choice of band patterns in the final survey. target choice for the European Space Agency’s (ESA) Comet A remaining concern with rolling cadence is the possibility that Interceptor (expected to launch in 2029; Snodgrass & Jones 2019), individual objects of interest may be missed, or more likely be in the unlucky case that a suitable long-period comet is missed for discovered later than they could have been if they first brighten a year. In general, discovery metrics for OCCs are not strongly above LSST detection limits in an off stripe. This could affected by rolling cadence, so this is not seen as a major risk for 40 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 29. Color light-curve metrics for observing strategies with nightly pairs in the same vs. different filters (v1.5 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. the mission. Further study of the effect of rolling cadence on how 4.6.1. Fraction of Time Devoted to DDFs early we might discover ISO and OCC targets is ongoing. The time balancing of DDF and WFD observations within the LSST has a noticeable impact on overall solar system detections and light-curve inversion capability. Simulations 4.6. Deep Drilling Field Observing with a larger portion of survey time for DDFs were previously The DDFs are a key component of LSST’s structure, currently trailed in the v1.6 sims (ddf_heavy_) but were rejected, as allocated ∼5% of the total survey time in the latest survey they produced significant negative impacts on all solar system simulation baselines. There are five confirmed DDF pointings populations and their metrics, as well as failing to meet some of (Table 6), which will be observed with a completely different the key science requirements for the WFD. cadence from the WFD: a higher sampling rate, as well as a The v2.0 simulations are the latest set of simulations that different sampling of filters (Jones et al. 2020). The locations of explore varying the fraction of total survey time allocated to the DDF pointings were largely motivated by both Galactic and DDFs. They test a more conservative variation of ±3% survey extragalactic science goals (Bell & Hermes 2018;Brandt etal. time spent on DDFs from the baseline value of 5%. In these 2018;Holwerdaetal. 2018; Scolnic et al. 2018; Capak et al. simulations, any extra observing time is evenly distributed 2019a). However, the ability to stack the denser sampling means across the remaining components of the LSST. Both options are satisfactory for the discovery and light-curve inversion of that these fields also provide a small, deeper data set than the solar system objects, for most or all populations (Figure 38). WFD (LSST Science Collaboration et al. 2009). The simulation with 8% of time allocated to the DDFs The DDFs provide a limited but strategic improvement to the (ddf_frac_ddf_per1.6 ) provides slightly worse results solar system science expected from LSST (Figure 38). The for discovery and light-curve inversion metrics compared to the extra depth of the stacked DDF data will improve the detectability of objects that are fainter than the WFD limits simulation with 3% survey time (ddf_frac_ddf_- (e.g., Smotherman et al. 2021) and thus either smaller or more per0.6 ), as WFD revisits are particularly important for distant. Four out of five of the DDFs are at ecliptic latitudes light-curve infill and for linking the motion of solar system >15° (Table 6), which means that they can only contain solar objects. However, both options are still within a negligible loss system objects on moderate-to-high-inclination orbits. These margin (<5%) on both metrics when compared to the baseline. objects are comparatively rare (Gladman & Volk 2021; Raymond & Nesvorný 2022), which will result in few observations of solar system objects in these four DDFs. The fifth field, COSMOS, is centered ∼9° from the ecliptic plane “1.6” indicates that the time allocated to DDFs is 1.6 times the baseline (Table 6): this lower latitude makes it sensitive to the mildly value. dynamically excited small body populations, so it is the DDF “0.6” indicates that the time allocated to DDFs is 0.6 times the baseline most likely to be directly beneficial for solar system science. value. 41 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 30. Investigating ways of reducing extra repeat visits and redistributing (v2.1 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. 4.6.2. “Rolling” DDFs the “stripes” of the WFD discussed in Section 4.5, these simulations instead alter the frequency of observation for each From the field population sensitivities, the key aspect of individual DDF and the relative weighting of time between solar system science interest is the choice of rolling cadence for DDFs. They include cases where specific DDFs are observed the COSMOS field and how it affects the small body metrics. only in certain years (e.g., only in the first 3 yr of LSST). The suggested types of DDF rolling cadences explored in the Between v2.1 and v2.2, a large number of DDF strategy v2.1 cadence simulations are unique to the DDFs: in contrast to 42 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. 2 2 Figure 31. Comparison of the sky coverage with greater than 825 deg (in cyan) and 750 deg (in black) for v2.0 cadence simulations with various options for third repeat visits. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. variations were considered, but in general solar system metrics pointings for extended periods of time, longer than other solar have not been produced for these runs. The impacts of system populations. For the LSSTCam FOV, the time in years variations of DDF strategy while keeping the overall envelope for a TNO to pass through the field is given by of allocated DDF time and field location approximately 3. 5 3.5 1.437 constant are expected to produce negligible changes. Once a t = =» ,4() -- 11 24 hr day ´ 365.25 day yr 2.435aa narrower range of DDF strategies are under consideration, solar 3600 system metrics will be produced and checked for potential impacts. Therefore, we consider how DDF “rolling” cadences where α is the opposition on-sky rate of motion in arcseconds would probably affect solar system science, with a specific per hour. For distances of 40–60 au, a TNO traverses the field focus on the highest-yield COSMOS DDF. in ∼5–8 months. In comparison, more distant TNOs (r … 200 The Jupiter Trojans will complete approximately one full orbit au) remain in the field for …2 yr. This means that the during the span of the LSST. The slightly asymmetric populations population of r ∼ 30 au TNOs observed in a DDF is refreshed lead and trail the giant planet in its orbit by ∼60°, with a mean ∼30 times during LSST as a result of (primarily Earth’s) orbital libration amplitude of 33° from the center of their respective motion, compared to r = 300 au TNOs, which would take a Trojan clouds (Marzari et al. 2002). The more populous L4 third of the full survey to pass through the field. COSMOS and, cloud’s inclination distribution is centered around the ecliptic at lower yield, the other DDFs thus provide multimonth TNO latitude of COSMOS, while the flatter L5 inclination distribution orbital arcs that would determine parameters r and i to a still encompasses COSMOS (Slyusarev & Belskaya 2014).The broad Jupiter Trojan libration distribution produces an on-sky precision useful for population studies. However, these arcs are distribution that has wide wings of consistent density around the generally too short to reduce uncertainties on a and e to levels libration centers. These orbital properties mean that Jupiter sufficient for Neptune resonance classification (Volk et al. Trojans will be visible in the COSMOS DDF during distinct 2016). Deep revisits by LSST around the DDFs in later years to several-hundred-day observation periods within LSST recover the DDF-sourced TNOs would be necessary for this (Figure 39). This will permit smaller-diameter Jupiter Trojans to additional improvement for outer solar system science. There- be discovered than can be achieved by the WFD. Therefore, it is fore, TNO science is flexible relative to the DDF “rolling essential that the COSMOS DDF is observed at times when the cadence” decision, as long as COSMOS and other DDFs are Jupiter Trojans are passing through the field. visited for approximately 2 yr at some point within LSST. Throughout the LSST, the COSMOS and other DDFs will provide a constantly refreshing sample of shift-and-stack- 4.7. Microsurveys detectable TNOs smaller than can be seen in single frames of −1 the WFD. As TNOs move slowly (<5″ hr for r > 30 au; see A wide variety of special small observing programs have Figure 25), they will remain in the sidereally static DDF been proposed by the Rubin Observatory user community that 43 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 32. Impact of various third visit scenarios (v2.0 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. We have truncated the y-axis for visibility in the light-curve inversion metrics plot. The fraction of the Jupiter Trojan detections in some of these runs compared to the baseline is lower than 0.4 and off the bottom edge. have been grouped together under the microsurveys category. and provide unique benefits not obtained from the larger Smaller than the minisurveys that have been incorporated into components of the LSST observing strategy. Some of these the LSST footprint, each microsurvey consumes between proposed microsurveys plan to observe new regions of sky not approximately 0.3% and 3% of the total available observing covered within the survey footprint, while others reobserve time. The microsurveys compliment the other components of regions of the sky already covered in the LSST footprint with a the LSST (WFD, DDFs, NES, and Galactic plane observing) separate observing strategy. Of all the proposed microsurveys, 44 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 33. Snapshot of the cumulative number of on-sky visits in all filters as a function of a subset of rolling cadence scenarios simulated at Year 3.5 (v2.0 simulations). The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. the one that is most relevant to the discovery and follow-up of past, Rubin Observatory’s large aperture size would put it in a minor planets and ISOs is the low-SE twilight survey, which unique position to provide a more sensitive search for several aims to take short exposures closer to the Sun in order to search populations of solar system objects such as IEOs, Earth for small bodies in an orbital phase space that the rest of the Trojans, and sungrazing comets than has been performed LSST is not sensitive to. previously (Seaman et al. 2018). NEOs in the region of the solar system interior to Earth’s orbit (including Atiras with orbits interior to the orbit of Earth and “‘Ayló’chaxnims with 4.7.1. Low Solar Elongation Solar System Twilight Microsurvey orbits interior to Venus” orbit ) are the least constrained portion of currently available NEO models owing to observa- The twi_neo family of simulations use 50% of the tional limitations of objects at low SE (Greenstreet et al. 2012; available observing time during morning and evening twilight to perform a microsurvey of the low-SE (40°  SE  60°) sky, which would otherwise not be observed during the WFD Objects on orbits entirely within the orbit of Venus have been previously referred to in the literature as Vatiras (Greenstreet et al. 2012). This name has observing cadence (see Figure 40). The opportunity for LSST acted as a placeholder until the first object in this population is discovered and to observe the low-SE sky during twilight is the only time when named. With the recent discovery and naming of the first inner-Venus object, viewing solar system objects inward to Earth is possible. ‘Ayló’chaxnim (Bolin et al. 2020c, 2022; Ip et al. 2022), we adopt the name of Although surveys similar in nature have been carried out in the this population after its first known member, as is tradition. 45 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 34. Snapshot from a video animation of the baseline_v2.0_10yrs to demonstrate how the two-band rolling cadence observing strategy is implemented over a 10 yr simulated LSST survey. The animation steps through in 30-day intervals over 10 yr, displaying the cumulative number of on-sky visits in all filters (left) and presenting the total number of on-sky visits in all filters accumulated during the time step (right). The animation has a real-time duration of 25 s. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. (An animation of this figure is available.) Figure 35. Snapshot from a video animation of the rolling_ns3_rw0.9_v2.0_10yrs to demonstrate how the three-band rolling cadence observing strategy is implemented over a 10 yr simulated LSST survey. The animation steps through in 30-day intervals over 10 yr, displaying the cumulative number of on-sky visits in all filters (left) and presenting the total number of on-sky visits in all filters accumulated during the time step (right). The animation has a real-time duration of 25 s. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. (An animation of this figure is available.) Granvik et al. 2018). In addition, recent observational evidence orbit determination of the often short-lived visitors (Bolin et al. and dynamical studies suggest that there are possible 2020b; Ye et al. 2020). Low-SE observations of LSST- metastable regions in the innermost portion of the solar system discovered ISOs would provide further opportunities beyond where more objects on orbits similar to that of ‘Ayló’chaxnim what the WFD observing cadence would offer for observing may be lurking and awaiting discovery (de la Fuente Marcos & possible mass shedding, outbursting, or breakup events of these de la Fuente Marcos 2020a; Greenstreet 2020; Popescu et al. interstellar interlopers, as well as extend the amount of time 2020; Bolin et al. 2022, 2023; Ip et al. 2022; Sheppard et al. these short-lived visitors can be observed. Monitoring the sky 2022). in the near-Sun region could also provide the opportunity to In addition to the discovery of IEOs, a LSST low-SE twilight observe cometary outbursting or breakup events as non- microsurvey could enhance the discovery of ISOs; ISO 2I/ interstellar near-Sun comets reach heliocentric distances Borisov was discovered during twilight by an amateur <1 au, which may otherwise not be characterized. Observing astronomer in 2019 (Borisov 2019). Routine observations at comets (with origins from either within our solar system or low SE could also provide prediscovery images of ISOs, interstellar space) as they reach the near-Sun region will better enabling additional astrometric measurements for improved inform us of how insolation can process cometary surfaces and 46 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 36. Snapshot from a video animation of the six_rolling_ns6_rw0.9_v2.0_10yrs to demonstrate how the three-band rolling cadence observing strategy is implemented over a 10 yr simulated LSST survey. The animation steps through in 30-day intervals over 10 yr, displaying the cumulative number of on-sky visits in all filters (left) and presenting the total number of on-sky visits in all filters accumulated during the time step (right). The animation has a real-time duration of 25 s. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. (An animation of this figure is available.) connect the near-Sun comet population to comets as a whole nights on/four nights off). Figures 41 and 42 show the impact (Seaman et al. 2018). of these low-SE twilight microsurvey cadence options on the Discoveries of PHAs can also be enhanced with the discovery and light-curve inversion solar system MAF metrics. inclusion of a low-SE twilight microsurvey, improving our We note that Figure 41 provides discovery completeness values knowledge of and increasing warning times for potential that are not normalized to the baseline simulation’s output since asteroid impacts. In addition, the possible discovery of more there are no ‘Ayló’chaxnims discovered with the baseline Earth Trojans, which librate at the Earth–Sun L4 and L5 survey cadence; this is the only figure that shows the outcomes Lagrange points, would improve our knowledge of planetary of the MAF metrics analysis that is not normalized to the impactor sources for both recent and ancient cratering events baseline simulation’s output. on Earth and the Moon (Seaman et al. 2018; Malhotra 2019; The discovery completeness for the ‘Ayló’chaxnim popula- Markwardt et al. 2020). Earth Trojans also make attractive tion (NEOs with orbits interior to the orbit of Venus) for a spacecraft mission targets owing to their low relative velocity variety of cadence options for this microsurvey are compared to with Earth. Lastly, observing asteroids in the near-Sun region that for the baseline survey (with no low-SE twilight with LSST can provide the opportunity to probe mechanisms microsurvey) in Figure 41. Each of the microsurvey cadence responsible for the supercatastrophic disruption of asteroids options provides a (sometimes much) higher discovery with small perihelia (closest orbital distance to the Sun; completeness than the baseline survey, which does not include Granvik et al. 2016) and test the extent to which this any ‘Ayló’chaxnim discoveries since ‘Ayló’chaxnims are only phenomenon occurs for asteroids that reach very small solar visible at SEs smaller than the WFD cadence reaches. In the top distances. panel of Figure 41, which uses the Rubin SSP discovery criteria Due to the large amount of science for a wide variety of of three nightly pairs in 14 days, the highest ‘Ayló’chaxnim small body populations that would be made possible with a discovery completeness is reached when the low-SE twilight low-SE twilight microsurvey, a family of runs executing a microsurvey is run every night with three repeat visits per variety of low-SE observing cadences during twilight have pointing in either iz or riz filters (i.e., twi_neo_repea- been included in the last few rounds of cadence simulations, the t3_iz_np1_v2.2_10yrs and twi_neo_repea- most recent of which are the v2.2 simulations. The v2.2 family t3_riz_np1_v2.2_10yrs). These microsurvey cadences is split into twi_neo and twi_neo_brightest, which would result in the completeness of the H „ 20.5 and the execute the minisurvey when the Sun is above −17°.8 and H „ 16.0 ‘Ayló’chaxnims increasing to ≈1.5% and ≈2%, −14° elevation, respectively. The twi_neo and twi_neo_- respectively, providing the potential for a significant increase in brightest simulations consist of 15 s exposures per visit the discovery of inner-Venus asteroids. Running the micro- and explore a variety of repeat visits, filters, and nightly survey with either three or four repeat visits during either the twilight on-off cadences. The twi_neo_repeatX_Y_npZ_- brightest twilight time (i.e., when the Sun is above −14° v2.2_10yrs and twi_neo_brightest_repeat- elevation) or full twilight time (i.e., when the Sun is above X_Y_npZ_v2.2_10yrs families consist of X repeat visits ( −17°.8 elevation) produces similar results in ‘Ayló’chaxnim i.e., triplets or quads; all separated by ∼3 minutes) in Y filter(s) discovery completeness across the various nightly “on”/“off” per pointing per twilight observed where Z = 1 (on every cadences. The largest discovery completeness increases for night),2 (one night on/one night off),3 (one night on/two these options occur when the microsurvey is run every night nights off),4 (one night on/three nights off),5 (four nights using either iz or riz filters, which result in increases of ≈1% to on/four nights off),6 (three nights on/four nights off),7 (two ≈1.5%. Simply using the z filter does not get as large of a 47 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 37. Impact of various rolling cadence scenarios (v2.0 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. The baseline simulation has a two-band rolling cadence implemented with no rolling in the Galactic plane and NES. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. Note: The light-curve inversion plot has been truncated for clarity. The Jupiter Trojans extend beyond the plot for the six_rolling_ns6_rw0.9_v2.0_10yrs. discovery boost as using either iz or riz filters. Unsurprisingly, If, unlike the Rubin SSP requirement of three nightly pairs in 14 days, four detections in a single night with four repeat visits the less often the microsurvey is run (fewer number of “on” per pointing are required, the discovery completeness improves nights), the lower the ‘Ayló’chaxnim discovery completeness further. This is more typical of observing cadences used for drops. 48 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 6 every night is completely detrimental, with up to 40% drops for Planned LSST Deep Drilling Fields H „ 15 Jupiter Trojans and 20% drops for H „ 18 MBAs compared to the baseline survey that does not include any low- Deep Drilling Field R.A. Decl. Ecliptic SE twilight microsurvey. Of the microsurvey options discussed (deg)(deg) Latitude above, the only option that does not drop the fraction of objects (J2000)(J2000)(deg) for which light-curve inversions can be obtained by >5% from ELAISS1 (European Large-Area ISO Sur- 9.450 −44.00 −43.18 that of the baseline survey (the level considered acceptable) vey-S1) field is twi_neo_repeat4_riz_np4_v2.2_10yrs, which XMM-LSS (X-ray Multi-Mirror Mission- 35.71 −4.75 −17.90 includes four repeat visits (i.e., quads) per pointing in riz Newton Large Scale Structure) field ECDFS (Extended Chandra Deep Field 53.13 −28.10 −45.47 filters, where the low-SE twilight microsurvey is run with a South) cadence of one night on/three nights off. This simulation keeps EDF-S (Euclid Deep Field South) 61.24 −48.42 −66.60 light-curve inversion at baseline level or above with up to a COSMOS (Cosmological Evolution Sur- 150.10 2.18 −9.40 30% increase above baseline levels for H „ 16 PHAs. As vey) field described above, this option also performs well for discovery completeness, which provides baseline-level performance or higher (up to ≈2%) for all included small body populations NEO discovery by current surveys (e.g., Gehrels & except the H „ 12 OCCs with a maximum perihelia of 20 au, Jedicke 1996; Larson et al. 2003; Tonry et al. 2018) Such a which again get a ∼0.5% discovery completeness drop. An cadence would require the development and implementation of additional benefit to having four repeat visits instead of three code outside SSP, which is designed only to use image pairs to repeat visits is better resiliency to contamination by satellites make tracklets. Under this alternative cadence, running the streaks (for additional discussion, see Section 5.4). microsurvey every night during the brightest part of twilight in In general, running the low-SE twilight microsurvey less either iz or riz (i.e., twi_neo_brightest_repea- frequently proves better for both discovery completeness and t4_iz_np1_v2.2_10yrs and twi_neo_brightes- light-curve inversion when all solar system small body t_repeat4_riz_np1_v2.2_10yrs in the bottom panel of populations are considered. Furthermore, running the low-SE Figure 41), the discovery completeness increases to ≈8% and twilight microsurvey at an infrequent cadence boosts discovery ≈9.5% for the H „ 20.5 and H „ 16.0 ‘Ayló’chaxnims, completeness overall, including for the ‘Ayló’chaxnims, which respectively. Given that the baseline ‘Ayló’chaxnim discovery also see a significant discovery completeness enhancement in completeness is zero, a low-SE twilight microsurvey thus has both discussed simulations (to ≈0.5%–0.75% for three repeat the potential for a dramatic shift in the discovery of asteroids visits in riz with a cadence of three nights on/four nights off or interior to the orbit of Venus. ≈0.15%–0.25% for four repeat visits in riz with a cadence of In contrast, Figure 42 (top panel) shows that for nearly all one night on/three nights off). Light-curve inversions are also other solar system small body populations, running the low-SE enhanced when the low-SE twilight microsurvey is run twilight microsurvey every night produces the largest drops infrequently (once every 3 days with four repeat visits per (≈3.5%) in discovery completeness, in particular for the fainter pointing in riz filters) compared to the baseline survey cadence. objects. This is because the low-SE twilight microsurvey takes Given these enhancements and the large amount of science for time away from the WFD observing that would otherwise be a wide variety of small body populations that would be made performed during those twilight hours. This produces a drop in possible with a low-SE twilight microsurvey, we thus strongly the discovery completeness for faint objects that would encourage an infrequently run low-SE twilight microsurvey to otherwise be discovered at larger SEs; faint objects are also be included in the LSST survey cadence from the start of the harder to see than brighter objects when looking near the Sun. survey. We note for the reader that the significant increases This drop is also increased when the microsurvey observations shown here from the discovery metrics when the twilight low- are only made with the z filter. On the other hand, bright SE microsurvey is included will not translate into the exact (H„ 16) NEOs and PHAs get the strongest discovery boost same gains in the actual LSST ‘Ayló’chaxnim, H „ 16 NEO, when the microsurvey is run every night since more of the easily and H „ 16 PHA discovery yields. It depends on the size and visible objects are picked up in the additional sky coverage. Of albedo distribution of these populations, which is not included all the twi_neo family simulations, the two best options for in the calculation of our discovery metrics (see Section 2.3.1). discovery completeness for all included small body populations The significant increase in our metrics does show that including are twi_neo_repeat3_riz_np6_v2.2_10yrs,which the microsurvey will significantly enhance LSST’s chances of includes three repeat visits (i.e., triplets) per pointing in riz finding new‘Ayló’chaxnims and other IEOs, but the actual filters where the low-SE twilight microsurvey is run with a number of new discoveries may be very small. cadence of three nights on/four nights off, and twi_neo_r- Given the numerous scientific benefits and enhancements in epeat4_riz_np4_v2.2_10yrs, which includes four repeat solar system discoveries and light-curve inversions that will visits (i.e., quads) per pointing in riz filters with a cadence of one come from running the low-SE twilight microsurvey, as night on/three nights off. In these simulations, the only described above, we recommend avoiding waiting to start this population to see a drop in discovery completeness are the microsurvey until Year 2 or later in the 10 yr survey. One H„ 12 OCCs with a maximum perihelia of 20 au, which get a additional reason for starting this microsurvey in Year 1 is the ∼0.5% discovery drop; all other populations either match the increasing number of satellite constellations being sent into low baseline or gain an increased discovery completeness (up to ∼3.5% and ∼2%, respectively). Earth orbit. These satellite constellations are most problematic When considering the ability to perform light-curve inver- for astronomic observations during twilight hours, when the sions for PHAs, NEOs, MBAs, and Jupiter Trojans (bottom numerous satellites are brightest in the sky (for further panel of Figure 42), running the low-SE twilight microsurvey discussion of the impact of satellite constellations on solar 49 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 38. Impact of varying the time allocated to the DDFs (v2.0 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. system science, see Section 5.4). With the number of satellite of satellite contamination as much as possible and enable the constellations continuing to increase, and plans for that increase most solar system science. The SCOC has recently made a to continue for years to come, the problem of contamination recommendation for a low-SE NEO twilight microsurvey to be will only get worse during the later years of the 10 yr LSST included in the survey strategy starting in Year 1 of LSST, with survey. We thus recommend starting the low-SE twilight further opportunities to explore the final details of the microsurvey in Year 1 of operations in order to reduce the level implementation (Bianco & the SCOC 2022). 50 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. within days of discovery and not require explicit self-follow-up by Rubin. It would then be interesting to examine this scenario in further detail (since it would not follow the traditional SSP detection method) and quantify the number and purity of high digest 2 tracklets (i.e., short-arc moving object detections likely to be solar system objects) Rubin would identify and report on a nightly basis, as well as the typical apparent magnitude of reported tracklets (i.e., assess whether the broader community’s NEO follow-up system would be able to keep up with this modified reporting method). 4.7.2. Other Microsurveys A number of additional microsurveys requiring 0.3%–3% of Figure 39. The number of Jupiter Trojan detections (of a simulated sample overall survey time were submitted in response to the 2018 population of 5000 objects) in the COSMOS DDF over the course of the baseline_v2.2_10yrs simulation. This simulation starts the LSST in white paper call on survey strategy. These microsurveys 2023 October. By 7 yr into the survey, both Jupiter Trojan clouds have include the following: traversed across the COSMOS DDF. 1. Adding extra fields: virgo (adds the Virgo Cluster −1 to WFD), carina (1 week yr in Carina Nebula), smc_movie (short g exposures in SMC for two nights), roman (covering the Roman bulge field twice per year). 2. local_gal_bindx ⟨I⟩: Covers 12 Local Group galaxies with extra gri exposures. 3. too_rate ⟨X⟩: Follows Targets of Opportunity, where X is the number per year, 4. north_stripe: Adds northern extension stripe up to decl. +30 (illustrated in the final panel of Figure 2). 5. short_exp: Takes up to 3 × 5 s exposures in Year 1 of the survey. 6. multi_short: Takes sequences of 4 × 5 s exposures in each filter, with the aim of obtaining 12 total sequences of short exposures per year, achieving ∼700 exposures per pointing. As shown in Figure 43, the effects of the majority of these Figure 40. Comparison of the number of visits as a function of SE (at the microsurveys on the discovery and light-curve metrics that are center of the FOV) with and without a low-SE solar system twilight of most concern for solar system science are minimal (5%) microsurvey. The bin size is 2°. The baseline simulation is baseline_- since this involves a very small fraction of the overall LSST v2.2_10yrs, and the selected low-SE solar system twilight microsurvey survey time. The exception to these generally minimal effects is simulation is twi_neo_repeat4_iz_np1_v2.2_10yrs. We note that part of the orange histogram for the simulation that includes the low-SE seen in the multi_short simulation (final column in twilight microsurvey is plotted underneath the blue histogram for the baseline Figure 43). This survey strategy causes a ∼5%–25% drop in simulation. the number of objects detected, particularly for the fainter solar system object populations (this outcome is to be expected with Finally, we look at possible further cadence enhancements the switch of 12 of the ∼82 exposures per pointing per year to and/or software improvements. All currently analyzed much shorter exposures). While the vast majority of the cadences assume either three or four repeat visits (i.e., triplets additional microsurveys have little to no effect on the metrics or quads), but the discovery criteria are the same as used for of most interest to solar system researchers, there remains the WFD observations (linking three tracklets over a 14-day possibility that the combination of several of these micro- period). Under such assumptions, the standard observing surveys could “constructively interfere” in such a way as to strategy of requiring pairs should be examined as well. Hints cause a large impact later. However, these microsurveys also that it may perform better are in Figure 41: note how repeat3 only impact a very small amount of the survey time and the cadences systematically produce more discoveries than overall survey strategy, and so the decision on the details of repeat4; a hypothetical repeat2 cadence may even further microsurveys may well also be delayed until later in the increase our sensitivity to ‘Ayló’chaxnims. We recommend cadence decision process. A combined set of microsurveys will that such cadences be simulated and analyzed. Should the analysis conclude that three- or four-tracklet likely be the subject of further simulation runs later when the cadences are still preferred, we would recommend that the larger and more influential parts of the cadence strategy are Rubin Observatory consider reporting such tracklets to the decided on. MPC immediately (within 24 hr). This would allow for third- party follow-up of such objects, which may be few enough and interesting enough that it is feasible that they may be followed https://www.lsst.org/submitted-whitepaper-2018 51 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 41. ‘Ayló’chaxnim (previously known in the literature as Vatira) population discovery completeness comparisons for cadences that include a possible low-SE solar system twilight microsurvey (v2.2 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. These metric values have not been normalized to the baseline’s simulation output since the baseline simulation, which does not include the low- SE twilight microsurvey, finds no ‘Ayló’chaxnims with three nightly pairs over 14 days. Top: discovery completeness for three nightly pairs in 14 days (the Rubin SSP discovery criteria). Bottom: discovery completeness for four detections in a single night for twilight simulations that take four visits per pointing. Simulation legend: twi_neo_repeatX_Y_npZ_v2.2_10yrs for a microsurvey with X repeat visits in Y filter(s) per pointing per twilight observed where Z = 1 (on every night),2 (one night on/one night off),3 (one night on/two nights off),4 (one night on/three nights off),5 (four nights on/four nights off),6 (three nights on/four nights off),7 (two nights on/four nights off). returns that are not yet captured in these simulations. The 5. Additional Considerations Not Explored in the Cadence survey cadence simulations do not account for the difference in Simulations Rubin Observatory operations in the first year of the survey The LSST cadence simulations are incredible tools for compared to later years. The growth of low-Earth-orbit satellite exploring the wide range of scenarios for how Rubin constellations and their future impact on LSSTCam observa- Observatory can survey the sky, but there are a few additional tions is not yet quantified. Targeted small observing programs potential factors that could enhance or impact the LSST science that take much less than 1% of the LSST observing time are not 52 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 42. Impact on other solar system metrics due to the inclusion of a low-SE solar system twilight microsurvey (v2.2 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. The baseline simulation does not include twilight low-SE observations. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. Simulation legend: twi_neo_repeatX_Y_npZ_v2.2_10yrs for microsurvey with X repeat visits in Y filter(s) per pointing per twilight observed where Z = 1 (on every night),2 (one night on/one night off),3 (one night on/two nights off),4 (one night on/three nights off),5 (four nights on/four nights off),6 (three nights on/four nights off),7 (two nights on/four nights off). commissioning of LSSTCam and the Simonyi Survey included in the cadence simulations, and opportunities to Telescope. propose to Rubin Observatory with these very small observing requests will be considered closer to the start of the survey 5.1. Incremental Template Generation in Year 1 (Ivezić & the SCOC 2021). Additionally, the combined benefits of the LSST data with future wide-field surveys The LSST cadence simulations and the MAF metrics assume cannot be derived from the cadence simulations alone. Some of that the first year of the survey will run exactly like later years, these considerations require analysis of test observations during but this is not quite the case. The data management pipelines 53 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 43. Impact of various microsurvey scenarios. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. The y-axis is truncated in the bottom plot at 1.6 for readability. The light-curve inversion NEO H = 19.0 and light-curve inversion PHA H = 19.0 extend to nearly 2.5 for the multi_short_v2.0_10yrs run. use difference imaging to identify transient sources within the template for the observed field must exist in the given filter of LSST exposures by subtracting off a template representing the the observation. Templates are expected to be produced during static sky. The Rubin SSP pipelines use these catalogs nightly the data processing of the yearly data release. A brief overview to identify moving sources as part of the prompt products data of how templates are likely going to be made from coadded processing (Jurić et al. 2021). In order for new solar system observations is available in the summary paper describing the objects to be discovered in real time during the survey, a LSST DESC DC2 (Dark Energy Science Collaboration second 54 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. data challenge) simulated sky survey (LSST Dark Energy on model orbits (Trilling et al. 2018a). Objects at these size Science Collaboration (LSST DESC) et al. 2021). Some fields ranges in the outer regions of the solar system are particularly will have enough observations in commissioning to generate underobserved by current surveys owing to their faint apparent templates at the start of the survey, but this will not be true for magnitudes, and new constraints from Rubin Observatory the vast majority of the sky. Year 1 of the LSST will have to be would provide vital information on planetary formation and treated differently if solar system bodies are to be detected collisional processes that have occurred and still occur in our nightly. Otherwise, these discoveries will only be made during solar system. An additional set of four return visits to these the data release processing to make the yearly detection deep fields over a 2 yr period is proposed that would enable catalogs from LSST only data. dynamical classification and color measurements (if one of the Within MAF there are no solar system metrics focused on later visits was taken in a different filter). In addition to the Rubin prompt data products. Whether or not a different exploring the nature of the observed TNO size distribution and template generation strategy is used in Year 1 of the LSST, the the physical properties of the TNOs on both sides of the broken total number of discoveries and the number of objects with power law, the proposed solar system DDFs would also sufficient observations for shape inversion will remain the provide further characterization of other solar system objects. same. These metrics only probe what would be available in the Densely sampled light curves of MBAs, Centaurs, and Jupiter yearly data release catalogs at the end of the 10 yr span of the Trojans can reveal any temporal variability in color and LSST. They do not quantify the impact on the study of brightness within the 2 hr observation period. From this, transient phenomena. If no templates are produced in the physical properties such as color, size, and shape can be LSST’s first year, all time-domain-related events (such as ISO constrained. apparitions, NEO/PHA close fly-bys, and cometary outbursts) The total program requires 40 hr of observing time over the present in the first year of survey observations would be 10 yr of the LSST (totaling =0.3% of total survey time), the discovered 6 months to 1 yr after they occurred. The duration equivalent of four winter observing nights. This request is well of these events is on the scale of days to weeks; discovering below the threshold for cadence variations to be evaluated by these events at the time of the first and second LSST data the SCOC and therefore has not been included in the LSST release would be too late to perform any additional observa- cadence simulation runs. We highlight this proposed program tional follow-up (such as obtaining spectra) with other here, as it would deliver unique science not achieved with the facilities. Schwamb et al. (2021) highlight in further detail TNO sample found in the WFD, NES, or DDFs. The SCOC some of the unique opportunities for solar system science has recommended to the Rubin Observatory Operations Team enabled in the first year of the LSST if templates are generated that a very small amount of survey time be allocated in a call and implemented into Rubin Observatory’s data management for proposals for observing requests at this scale once the pipelines. survey performance has been evaluated (Ivezić & the The Rubin Observatory Operations Team has committed to SCOC 2021). producing templates incrementally during the first year of the LSST (Guy et al. 2021), but the exact requirements for 5.3. Euclid Synergies producing these Year 1 templates have not yet been decided. The specific strategy used will impact which observations SSP The Euclid Deep Field South is the fifth DDF to be adopted can search before the data release 1 processing and to what into the LSST after it was proposed in a written response to the limiting magnitude. It will also impact the productivity of the 2018 LSST Cadence Optimization White Paper Call (Capak low-SE solar system twilight microsurvey (described in et al. 2019a). This DDF will overlap with the southern deep Section 4.7.1), if the SCOC recommends the microsurvey to field observed as part of the upcoming ESA Euclid mission be included in the LSST year 1 observing strategy. The (Laureijs et al. 2011; Amiaux et al. 2012). Euclid aims to map microsurvey gains most benefit if astrometric follow-up the geometry of the dark universe during its 6 yr visible and observations can be performed by other observing facilities in near-infrared photometric and spectroscopic survey and will tandem with the LSST observations. Exploring the implications provide complementary observations to LSST’s wide-field of various incremental template generation strategies is beyond visible ground survey for a number of the LSST science goals. the scope of this paper, but this analysis should be carried out While only solar system objects with ecliptic latitudes beyond before the end of Rubin Observatory’s commissioning period. ±15° will be observed by Euclid, the science returns from its near-infrared capabilities, high angular resolution, and densely sampled light curves will still be significant (Carry 2018). 5.2. Solar System Deep Fields Previous studies suggest that the entire combined Rubin-Euclid Trilling et al. (2018a) proposed dedicated solar system data set would allow for the spectral classification of roughly “DDFs” in response to the 2018 LSST Cadence Optimization 150,000 solar system objects largely unknown to date and White Paper Call. This observing program was different in provide constraints on shape, rotation, activity, and binarity for scope than the typical DDFs currently imagined as part of a significant number of asteroids, Centaurs, and TNOs LSST and described in Section 4.6. Thus, we will refer to these (Carry 2018; Snodgrass et al. 2018; Guy et al. 2022).In pointings as solar system Deep Fields instead. This proposed particular, contemporaneous observations from both Euclid and program would consist of five different pointings at a range of LSST will allow rapid determination of orbits, which is vital for ecliptic latitudes, including coverage of parts of the leading and the recovery and follow-up of rare solar system objects trailing Neptune Trojan clouds. Each solar system deep field (Snodgrass et al. 2018). For more details on proposed Rubin- would be observed for 2.1 continuous hours in a single filter to Euclid derived data products see Guy et al. (2022). While reach the image depth (r = 27.5 mag; 3 mag deeper than the LSST can and will adapt its nightly observation schedule to WFD + NES observations) required to observe TNOs as small local weather conditions, the cadence and pointings of Euclid as 25 km in diameter through shifting and stacking the images are fixed and, therefore, known well in advance. As such, it 55 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. would fall to LSST to optimize its nightly schedule to surface brightness into sky that may later be the source of maximize the number of near-simultaneous pointings with detections when images are stacked; and they reduce the Euclid. Although an initial assessment of the simultaneous dynamic range available for the lower-S/N solar system astrometry between LSST and Euclid has been performed and detections. presented at the Rubin Project and Community Workshop in A recent study by Hu et al. (2022) finds that ∼10% of all 2019, no simulations currently exist that would quantify the LSST images will have a streak from a launched or planned-to- impact of newer LSST cadence simulations with respect to be-launched Starlink (Generation 1 or 2) or OneWeb satellite, joint Euclid-Rubin solar system science. with observations in twilight more frequently impacted. If significantly more satellites are launched over the next several years, more LSST observations could have streaks. The effects 5.4. Unquantified Considerations: Satellite Constellations of satellite constellations have not yet been comprehensively It is now certain that the accelerating industrialization of quantified for Rubin Observatory’s solar system science, and near-Earth space will have major adverse effects on astronom- we do not attempt to do so here, as no cadence simulations yet ical observation (Walker et al. 2020a, 2020b, 2021; Hall et al. include impacts from these satellite streaks on LSSTCam: we 2021; Halferty et al. 2022). The future density of global merely highlight a number of potential projected loss effects. satellite constellations is as yet uncertain, as it depends on First, the shallowing of LSST will decrease the detected solar commercial and regulatory decisions. However, regulatory system populations across any and all cadences. Across all approval has been granted by the United States for at least populations, detection loss for individual objects will deplete 30,000 satellites (Walker et al. 2021); >2500 of these launched the sparse light curves LSST generates, for instance, limiting in the past 3 yr, with 212 Starlinks in 2022 May alone. This the ability to detect small body activity (Section 2.3.2). Second, means that Rubin Observatory will have to observe into a there will be population-dependent losses in solar system hyperindustrialized sky. The first few years of LSST will science from satellite effects. In particular, for detections of contain the effects of the iterative passes of at least 6000 low- NEOs, which individually only become visible for a small Earth-orbit satellites—and as constellation build-out continues, subset of time within the span of LSST, the steep size satellite density will only increase throughout the survey. distribution means that the majority of detections are made For LSSTCam, there are notable streak effects when toward the Rubin magnitude limit. NEO discovery by the SSP illuminated satellites cross the focal plane during an LSST pipelines requires a pair of detections (see tracklets) and is thus exposure (Tyson et al. 2020). The level to which a streak could fragile to satellite effects: losing single detections from a pair be partly or fully saturated in the LSST images depends on has a disproportionate impact on the detectability of this each satellite’s orbit, morphology, reflectance properties, and population, as for intranight cadence outcomes (see orientation; the severity can vary through time, such as when a Sections 4.4.1, 4.4.3, and 4.4.4). Similarly, satellite-generated single launch’s “train” of co-released satellites are on its orbit detection loss will also acutely affect solar system populations raise and are brighter than when on final orbit. In these cases, that are only visible for week-to-month time periods, such as and also when the satellite is fainter and so the streaks are not ISOs and newly active comets. The seasonality of satellite saturated, the impacted pixels will not be suitable for density—more satellites are illuminated, and for longer, in photometry: each satellite streak decreases the effective sky summer—will have a seasonal impact on solar system object coverage of the exposure. Satellites at m ; 7, with bright- detectability (Hainaut & Williams 2020; McDowell 2020; nesses below saturation though at S/N; 100, are also Lawler et al. 2022). Seasonality detection biases adversely anticipated to create substantial multiorder cross talk. These affect all solar system populations that cluster on parts of the highly correlated linear “ghost” streaks center on cores sky (e.g., Trojan populations, resonant TNOs, potentially the surrounded by wings several hundred pixels in extent. The high-q, high-a TNOs, NEOs, and ISOs); they require careful degree to which Rubin Observatory’s processing pipelines will debiasing to generate accurate population models (e.g., be able to mask is yet to be determined. Even if algorithms can Kavelaars et al. 2020). While not quantified for satellites, be developed for adequate cross-talk removal, spatially general outcomes of inducing this type of effect can be correlated noise will still generate systematics throughout the considered in the vary_NES cadence family (Section 4.1.3). entire LSST data set (Tyson et al. 2020). Additionally, there is Additionally, twilight-bright satellites will be abundant in the an increase in global sky brightness from the ensemble of the low-SE sky that is being targeted for detection of PHAs, IEOs, size distribution of space debris and satellites—which has gravitationally focused ISOs, and comet comae. Illuminated already raised the diffuse zenith luminance by 10% as of 2021 satellites are most numerous near the horizon close to dawn and (Kocifaj et al. 2021). While the community focus to date has dusk; the low-SE twilight microsurvey runs in −12° solar been on the direct solar illumination of Earth-orbiting objects, elevation and darker. For the low-SE twilight microsurvey satellites also reflect moonlight moonshine, and potential (Section 4.7.1), the effects of satellite constellations will be additional sources of illumination (e.g., earthshine, mutual particularly pronounced, with some 90% of images expected to reflectance among illuminated space objects) have yet to be be impacted with at least one streak per image (Hu et al. 2022). modeled in published studies. The industrially caused loss of It may be possible for the Rubin Observatory scheduler to global darkness will continue as more anthropogenic material is selectively observe specific pointings on the sky, which could added to Earth’s orbital environment. For solar system science, decrease the number of WFD images with satellite streaks by a satellites and associated debris will thus have three main factor of two. However, this would come at the substantive cost of effects. They obliterate, or modify in unquantified ways, the photometry of individual object detections that are blazed over ∼10% of the LSST observing time (Hu et al. 2022). The trade- by streak footprints; they introduce systematic errors at low offs of implementing this algorithm would depend on the impact of the satellite streaks on LSSTCam, which, as noted above, has https://www.planet4589.org/space/stats/star/starstats.html yet to be fully characterized, and the number, sky distribution, and 56 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. apparent magnitude of satellites at the start of Rubin science 9. Similarly, increasing the number of exposures in bluer operations. Overall, the characterization of the impacts of satellite filters (u and g) decreases the number of faint objects for constellations on the LSST cadences, given the ever-changing which light-curve inversion is possible. parameters of constellation design, replenishment, and dynamic 10. Restricting repeat nightly visits to the same filter does not operation, will prove challenging. It will require major effort to significantly improve solar system metrics over mixed- model and incorporate into future LSST survey simulations. filter pairs. 11. Including a third visit of a field in the same night can have a very serious effect on the coverage area and other 6. Conclusions solar system metrics, particularly if the “Presto-Color” (rapid color; Bianco et al. 2019) strategy is implemented. By analyzing the LSST cadence simulations and the outputs for Adding the third visit in the same color as the earlier pair a suite of MAF metrics, we have explored the impact on solar and increasing the gap from the initial pair is shown to system science for a wide range of potential LSST cadences. Our have a much lower impact. resulting analysis highlights the importance of simulating the 12. Having the Rubin scheduler better balance extra nightly expected small body detections for future multipurpose wide-field visits beyond pairs in a given night by redistributing them surveys. This allows for tensions between main science drivers to across the sky has some benefit to small body discovery be identified in order to optimize the on-sky observing and with typically small hits to light-curve characterization. maximize the output from next-generation astronomical surveys 13. Rolling cadence strategies are generally positive for solar and facilities. We hope that this paper and the entire focus issue system metrics, although the most extreme rolling that this paper contributes to may serve as resources for future patterns (many stripes or very strong rolling) should be SCOC reviews of the LSST cadence, as well as for future wide- avoided. A rolling pattern that ensures a minimum field survey design. coverage to enable discovery of rare types of objects in In general, we find that a wide range of LSST survey the “off” stripes should be considered. strategies provide satisfactory temporal and spatial coverage to 14. Spending 3%–8% of the survey time on DDF observations achieve the goals for solar system science outlined in the SSSC produces only minimal losses for solar system science. If Science Roadmap (Schwamb et al. 2018a). Below, we some DDFs are observed only in certain years, observing summarize our key findings and recommendations based on the version 1.5–2.2 LSST cadence simulations: the COSMOS DDF for at least 2 yr would be the most beneficial for the detection and orbit characterization of 1. Observing the northern regions of the ecliptic up to +10° small bodies discovered by shift-and-stack algorithms. ecliptic latitude (the NES) is crucial for outer solar system 15. The COSMOS DDF should be observed when the Jupiter science and probing the solar system small body popula- Trojans are passing through the field, which occurs in tions that are asymmetrically distributed on the sky. discrete windows during LSST. 2. Covering the NES to at least 25% of the WFD level is 16. Starting a low-SE twilight microsurvey in Year 1 of critical for discovering and characterizing slowly moving operations would make Rubin Observatory uniquely objects (e.g., TNOs) and faint inner solar system objects. sensitive to several populations of solar system small 3. Shifting time away from the WFD to the Galactic plane bodies such as IEOs, Earth Trojans, and sungrazing can negatively impact light-curve measurements of faint comets and give the LSST the potential to improve solar system objects. asteroid models, test the theory of asteroid supercatas- 4. Shifting the WFD footprint northward from high- trophic disruption at small perihelion distances, and extinction regions to low-extinction sky, such that part improve asteroid impact warning times. An infrequently of the NES region obtains visits at WFD cadence, is a run (e.g., every three nights) low-SE twilight microsurvey welcome change. The new expanded northern WFD + would also enhance small body discovery and light-curve NES footprint used in the v2.0–v2.2 cadence simulations inversion and enable the discovery of ‘Ayló’chaxnims, is conducive to solar system science. which are only visible during twilight. 5. We advocate for moving from 2 × 15 s snaps to a single 17. The vast majority of the additional microsurveys for 1 × 30 s exposure per visit owing to the resulting ∼8% specific regions of sky have little to no effect on the solar boost in on-sky visits. system metrics, but there remains the possibility that 6. Aiming for 33-minute separations between nightly pairs combining several of these microsurveys could produce a is an ideal compromise between achieving a high pair result that causes a large impact later. This will likely completion rate per night and for the Rubin SSP pipelines need to be the subject of further simulation runs later to be sensitive to moving objects at distances up to when the larger and more influential parts of the cadence ∼150 au. strategy are decided on and actual operational overheads 7. Shorter exposure times are beneficial for the discovery of are measured from commissioning activities. PHAs and NEOs, while longer exposures are better for 18. The production of incremental templates in the first year the discovery of more distant objects. Shorter exposures of the LSST is particularly important for the timely also increase the total number of on-sky visits per follow-up of ISO apparitions and other transient solar pointing, providing denser sampling for light-curve system phenomena. Further work is needed to explore the inversion. A compromise between discovery and color/ light-curve characterization is to use 30 s exposures per impact on Year 1 discovery rates for the different visit when possible. potential options for creating the incremental templates. 8. Longer u-band exposures (50 s and above) tend to reduce 19. A 40 hr program as described in Trilling et al. (2018a) to discoveries at small sizes (fainter H) and are detrimental observe a set of solar system Deep Fields would probe the to light-curve inversions. small size end distribution of the TNO and Neptune 57 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Trojan populations that cannot be achieved with the Discovery Fellowships from New Zealand Government funding, currently planned LSST. administered by the Royal Society Te Apārangi. M.S.K. was 20. Creating joint data products with other contemporaneous supported by the NASA Solar System Observations program surveys such as ESA’s Euclid would be of great scientific (80NSSC20K0673). H.W.L. is supported by NASA grant benefit to the solar system science community. Apart NNX17AF21G and by NSF grant AST-2009096. T.D. acknowl- from overlapping DDFs, which are already planned, we edges support from the LSSTC Catalyst Fellowship awarded by suggest synchronizing observations of survey fields LSST Corporation with funding from the John Templeton where possible when choosing LSST nightly cadences. Foundation grant ID No. 62192. S.G. acknowledges support 21. The impact of rapidly increasing industrial activity in near- from the DIRAC Institute in the Department of Astronomy at the Earth space is not modeled here, but the projected adverse University of Washington. The DIRAC Institute is supported effects are substantial. We advocate for careful character- through generous gifts from the Charles and Lisa Simonyi Fund ization of the anthropogenic impacts on the LSST. for Arts and Sciences and the Washington Research Foundation. S.G. also acknowledges support from the Preparing for Astro- Our analysis has focused on the individual impact of changing physics with LSST Program funded by the Heising Simons at the same time one or two observing constraints or parameters Foundation (grant 2021–2975),from NSF (grant OAC-1934752), within the Rubin scheduler. We have not explored the impact of and from NASA (grant 80NSSC22K0978). The work of S.R.C. changing all these parameters simultaneously. We note that was conducted at the Jet Propulsion Laboratory, California although tuning individual knobs for the LSST survey strategy by Institute of Technology, under a contract with the National themselves may have little effect, the combination of several of Aeronautics and Space Administration. R.C.D. acknowledges them may not. This should be carefully considered by theSCOC support from the UC Doctoral Scholarship and Canterbury when they finalize their recommendation for the initial LSST Scholarship administered by the University of Canterbury, a PhD observing cadence. The analysis presented here should be research scholarship awarded through M.T.B.’s Rutherford repeated with the finalized LSST SCOC recommended observing Discovery Fellowship grant, and an LSSTC Enabling Science strategy when it becomes publicly available. Future cadence grant awarded by LSST Corporation. R.M. acknowledges support simulations should be generated and studied to examine additional from NSF (AST-1824869) and NASA (80NSSC19K0785).L.I. options for the low-SE twilight microsurvey. Further investigation acknowledges support from the Italian Space Agency (ASI) is also needed to explore how the various options for incremental within the ASI-INAF agreements I/024/12/0 and 2020-4-HH.0. template generation will impact the potential for real-time This material or work is supported in part by the National discovery and follow-up of our solar systemʼs minor planets Science Foundation through Cooperative Agreement AST- andISOsinthe first year of the LSST. This will be particularly 1258333 and Cooperative Support Agreement AST1836783 important for assessing whether the low-SE twilight survey should managed by the Association of Universities for Research in be included as part of the Year 1 LSST observing strategy. Astronomy (AURA) and the Department of Energy under contract No. DE-AC02-76SF00515 with the SLAC National The authors wish to acknowledge all of the essential workers Accelerator Laboratory managed by Stanford University. who have put their health at risk since the start of the COVID- For the purpose of open access, the author has applied a 19 global pandemic and the researchers who worked tirelessly Creative Commons Attribution (CC BY) license to any Author to rapidly develop COVID-19 vaccines. Without all their Accepted Manuscript version arising from this submission. efforts, we would not have been able to pursue this work. Data Access: Data used in this paper are openly available We thank the LSST Solar System Science Collaboration for from the Vera C. Rubin Observatory Construction Project and manuscript feedback. The authors thank Mike Brown for useful Operations Teams via https://github.com/lsst-pst/survey_ discussions. We thank the anonymous referee for reading and strategy/tree/main/fbs_1.7 and https://github.com/lsst-pst/ reviewing this very long manuscript and providing constructive survey_strategy/tree/main/fbs_2.0. The rubin_sim/OpSim feedback. The authors also acknowledge the SCOC for their LSST cadence simulation databases are available at https:// service to the Rubin user community. We thank Federica s3df.slac.stanford.edu/data/rubin/sim-data/. Bianco and the American Astronomical Society (AAS) Facility: Rubin. Journals editorial team for facilitating the Rubin LSST Survey Software: LSST Metrics Analysis Framework (MAF, Jones Strategy Optimization ApJS focus issue. et al. 2014), Astropy (Astropy Collaboration et al. This research has made use of NASA’s Astrophysics Data 2013, 2018, 2022), Numpy (van der Walt et al. 2011; Harris System Bibliographic Services. et al. 2020), Matplotlib (Hunter 2007), Pandas (pandas This work was supported in part by the LSSTC Enabling development team, T 2020), rubin_sim/OpSim (Naghib et al. Science grants program, the B612 Foundation, the University of 2019; Jones et al. 2020; Yoachim et al. 2022), sbpy (Mommert Washington’sDiRAC (Data-intensive Research in Astrophysics et al. 2019), JupyterHub (https://jupyterhub.readthedocs.io/ and Cosmology) Institute, the Planetary Society, and Adler en/latest), Jupyter Notebook (Kluyver et al. 2016), Python Planetarium through generous support of the LSST Solar System (https://www.python.org), OpenOrb (Granvik et al. 2009), Readiness Sprints. M.E.S. was supported by the UK Science scipy (Virtanen et al. 2020), healpy (Górski et al. 2005; Zonca Technology Facilities Council (STFC) grant ST/V000691/1, and et al. 2019), seaborn (Waskom 2021). she acknowledges travel support provided by STFC for UK Author Contributions: M.E.S. organized and coordinated the participation in LSST through grant ST/N002512/1. K.V. paper writing effort, as well as the review of the LSST cadence acknowledges support from the Preparing for Astrophysics with LSST Program funded by the Heising Simons Foundation (grant simulations and drafting of formal SSSC feedback to the SCOC 2021–2975),from NSF (grant AST-1824869), and from NASA that this work is derived from. She wrote the abstract, (grants 80NSSC19K0785, 80NSSC21K0376, and 80NSS Sections 1, 2.3.4, 3, 4.1.1, 4.1.2, 4.2.1, 4.4.1, 5.1, and the C22K0512). M.T.B. appreciates support by the Rutherford preambles to Sections 4, 4.1, 4.2, 4.4, 4.7, 5. She also cowrote 58 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Sections 6 and 5.2. She generated the figures showing the Appendix A footprints, discovery, and light-curve metrics, as well as color List of Acronyms light-curve metrics based on software utilities and jupyter A list of acronyms used within this paper is contained in notebooks developed by R.L.J. and P.Y. She also contributed Table 7. to the Planet Nine figures (Figures 6 and 7). She also created Tables 2–6. She also provided feedback on the entire manuscript. R.L.J. and P.Y. provided guidance on the jupyter notebook templates used to develop the paper figures. They Table 7 provided expert feedback on the performance and behavior of List of Acronyms Used in This Paper the Rubin scheduler and metrics. They also contributed to the Acronym Expansion discussions about the MAF metric outputs for all the cadence CFEPS Canada–France Ecliptic Plane Survey simulation families. They also contributed to Figure 24. R.L.J. COSMOS Cosmic Evolution Survey wrote Sections 2.2, 2.3, and subsections within. R.L.J. created CSV comma-separated values Tables 1 and 9 and produced the key plots for Figures 1, 6, and DDF Deep Drilling Fields 7. P.Y. wrote Section 2.1 and generated Figure 39. K.V. wrote DES Dark Energy Survey Sections 4.1.3 and 4.4.2, created Table 7, and provided DESC DC2 Dark Energy Science Collaboration second data challenge feedback on the whole manuscript. R.C.D. wrote Section 4.6 DESI Dark Energy Spectroscopic Instrument and cowrote Sections 5.2 and 5.3. C.O. wrote Sections 4.3 and DIA difference imaging analysis 4.2.2 and provided feedback on the overall manuscript. S.G. ECDFS Extended Chandra Deep Field South EDF-S Euclid Deep Field South aided in reviewing the LSST cadence simulations and drafting ELAISS1 European Large-Area Infrared Space Observatory Survey-S1 the formal SSSC feedback to the SCOC. In particular, she led ETNO extreme trans-Neptunian object the review and formal feedback for the low-SE twilight NEO FOV field of view microsurvey, soliciting and organizing discussion and feedback GP Galactic plane (used in figures) from the NEOs and ISOs SSSC working group. She wrote IEO inner-Earth object Section 4.7.1, cowrote Section 6, and provided feedback on the IOC inner Oort Cloud object overall manuscript. T.L. wrote Sections 4.4.4 (Third Visits in a ISO interstellar object Night) and 4.7.2 (Other Microsurveys), contributed to JFC Jupiter-family comet LC light curve (used in figures) Section 6 (Conclusions), and provided feedback on the overall low-SE low solar elongation manuscript. C.S. wrote Section 4.5 and provided feedback on LSST Legacy Survey of Space and Time the overall manuscript. B.T.B. wrote Section 4.2.3 (Other LSSTCam Legacy Survey of Space and Time Camera Variations of Exposure Times) and provided feedback on MAF Metrics Analysis Framework Sections 4.4.4 (Third Visits in a Night) and 4.7.1 (Low-SE MBA main-belt asteroid Solar System Twilight microsurvey). L.I. wrote Section 4.4.3, MBC main-belt comet contributed to the discussion presented in Section 4.2.2, and MDP Markov decision process provided feedback on the overall manuscript. M.T.B. wrote MMR mean motion resonance MOID minimum orbit intersection distance Section 5.4 and cowrote Section 4.6. S.E. led the writing of MPC Minor Planet Center Section 5.3, provided input on Section 5.4, and contributed the NEO near-Earth object description of the simulated ‘Ayló’chaxnim population in NES Northern Ecliptic Spur Section 2.2. M.S. provided feedback discussion and maintained OCC Oort Cloud comet the list of simulations across the manuscript and figures. M.S. OpSim operations simulation K. wrote the introduction to Section 2, developed the cometary Pan-STARRS Panoramic Survey Telescope and Rapid Response System brightening function implemented in the OCC metric, provided PHA potentially hazardous asteroid input on the OCC simulations, created the orbit OCC files, and PSF point-spread function S3M Synthetic Solar System Model provided feedback on the overall manuscript. M.J. contributed SCOC Survey Cadence Optimization Committee text to Section 4.7.1 and provided feedback on the overall SCP south celestial pole (used in figures) manuscript. H.W.L. created Figure 5. A.T., D.R., M.M.K., R. SED spectral energy distribution M., T.D., and Q.Y. provided feedback on the overall manu- SE solar elongation script. M.G. contributed to the discussions about astrometric SMLV Stars, Milky Way, and Local Volume precision and orbital characterization for Section 2.3.4 and S/N signal-to-noise ratio provided feedback on the overall manuscript. C.L. provided SRD Science Requirements Document feedback on Section 2. P.H.B. and W.J.O. contributed to SSP solar system processing discussions about the Planet Nine discoverability. S.R.C., J.D., SSSC Solar System Science Collaboration TNO trans-Neptunian object D.R., W.C.F., and A.T. contributed to the development of TVS Transients and Variable Stars light-curve metrics. W.C.F. also provided the TNO SED. M.E. WFD Wide–Fast–Deep S., with contributions from R.L.J., M.J., S.G., P.Y., S.E., M.S., XMM-LSS X-ray Multi-Mirror Mission-Newton Large Scale Structure and M.T.B., drafted the response to the referee report and ZTF Zwicky Transient Facility revised the manuscript based on the referee’s feedback. 59 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Appendix B List of LSST Cadence Simulations Table 8 contains a list of the LSST survey strategy simulations used in this work. Table 8 Table of All Simulations Referenced in This Work, Its Family of Simulations, the Baseline Simulation That It Should Be Compared Against, and Which Figures in the Paper Reference the Simulation Simulation Name Family Comparison Baseline Included in Which Figures baseline_nexp1_v1.7_10yrs Baseline N/A 17 baseline_nexp2_v1.7_10yrs Baseline N/A 9, 11, 17, 18, 23, 26 baseline_retrofoot_v2.0_10yrs Baseline N/A 2, 8 baseline_samefilt_v1.5_10yrs intranight baseline_v1.5_10yrs 28, 29 baseline_v1.5_10yrs Baseline N/A 4, 10, 28, 29 baseline_v2.0_10yrs Baseline N/A 2, 8, 12, 13, 14, 19, 27, 31, 32, 33, 34, 37, 38, 43 baseline_v2.1_10yrs Baseline N/A 1, 2, 6, 7, 15, 16, 20, 22, 30 baseline_v2.2_10yrs Baseline N/A 41, 42, 39 bluer_indx0_v2.0_10yrs bluer balance baseline_v2.0_10yrs 19 bluer_indx1_v2.0_10yrs bluer balance baseline_v2.0_10yrs 19 bulges_bs_v1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 bulges_bulge_wfd_v1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 bulges_cadence_bs_v1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 bulges_cadence_bulge_wfd_v1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 bulges_cadence_i_heavy_v1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 bulges_i_heavy_v1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 cadence_drive_gl100_gcbv1.7_10yrs filter_cadence baseline_nexp2_v1.7_10yrs 23 cadence_drive_gl100v1.7_10yrs filter_cadence baseline_nexp2_v1.7_10yrs 23 cadence_drive_gl200_gcbv1.7_10yrs filter_cadence baseline_nexp2_v1.7_10yrs 23 cadence_drive_gl200v1.7_10yrs filter_cadence baseline_nexp2_v1.7_10yrs 23 cadence_drive_gl30_gcbv1.7_10yrs filter_cadence baseline_nexp2_v1.7_10yrs 23 cadence_drive_gl30v1.7_10yrs filter_cadence baseline_nexp2_v1.7_10yrs 23 carina_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 ddf_frac_ddf_per0.6_v2.0_10yrs ddf percent baseline_v2.0_10yrs 38 ddf_frac_ddf_per1.6_v2.0_10yrs ddf percent baseline_v2.0_10yrs 38 filterdist_indx1_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21, filterdist_indx2_v1.5_10yrs Baseline N/A 4, 10, 21 filterdist_indx3_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21 filterdist_indx4_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21 filterdist_indx5_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21 filterdist_indx6_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21 filterdist_indx7_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21 filterdist_indx8_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21 footprint_0_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_1_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_2_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_3_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_4_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_5_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_6_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_7_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_8_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_add_mag_cloudsv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_big_sky_dustv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_big_sky_nouiyv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_big_skyv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_big_wfdv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_bluer_footprintv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_gp_smoothv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_newAv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_newBv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_no_gp_northv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 good_seeing_gsw0.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_gsw1.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 60 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 8 (Continued) Simulation Name Family Comparison Baseline Included in Which Figures good_seeing_gsw10.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_gsw20.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_gsw3.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_gsw50.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_gsw6.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw0.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw1.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw10.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw20.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw3.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw50.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw6.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 local_gal_bindx0_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 local_gal_bindx1_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 local_gal_bindx2_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 long_gaps_nightsoff0_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff0_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 31 long_gaps_nightsoff1_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff1_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff2_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff2_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff3_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff3_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff4_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff4_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff5_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff5_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff6_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff6_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff7_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff7_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_np_nightsoff0_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff0_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff1_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff1_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff2_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff2_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff3_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff3_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff4_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff4_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff5_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff5_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff6_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff6_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff7_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff7_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_u1_v2.0_10yrs longer u visits baseline_v2.0_10yrs 19 long_u2_v2.0_10yrs longer u visits baseline_v2.0_10yrs 19 multi_short_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 no_repeat_rpw-1.0_v2.1_10yrs suppress repeats baseline_v2.1_10yrs 30 no_repeat_rpw-10.0_v2.1_10yrs suppress repeats baseline_v2.1_10yrs 30 no_repeat_rpw-100.0_v2.1_10yrs suppress repeats baseline_v2.1_10yrs 30 no_repeat_rpw-2.0_v2.1_10yrs suppress repeats baseline_v2.1_10yrs 30 no_repeat_rpw-20.0_v2.1_10yrs suppress repeats baseline_v2.1_10yrs 30 no_repeat_rpw-5.0_v2.1_10yrs suppress repeats baseline_v2.1_10yrs 30 noroll_v2.0_10yrs rolling baseline_v2.0_10yrs 33, 37 north_stripe_v2.0_10yrs microsurveys baseline_v2.0_10yrs 2, 43 pair_times_11_v1.7_10yrs pair_times baseline_nexp2_v1.7_10yrs 26 pair_times_22_v1.7_10yrs pair_times baseline_nexp2_v1.7_10yrs 24, 26 pair_times_33_v1.7_10yrs pair_times baseline_nexp2_v1.7_10yrs 24, 26 pair_times_44_v1.7_10yrs pair_times baseline_nexp2_v1.7_10yrs 26 pair_times_55_v1.7_10yrs pair_times baseline_nexp2_v1.7_10yrs 24, 26 61 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 8 (Continued) Simulation Name Family Comparison Baseline Included in Which Figures pencil_fs1_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint pencil_fs2_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 2, 15, 16 footprint plane_priority_priority0.1_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.1_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.2_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.2_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.3_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.3_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.4_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.4_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.6_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.6_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 2, 15, 16 footprint plane_priority_priority0.9_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.9_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority1.2_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority1.2_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint presto_gap1.5_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 2, 31, 32 presto_gap1.5_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap2.0_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap2.0_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap2.5_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap2.5_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap3.0_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap3.0_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap3.5_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap3.5_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap4.0_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap4.0_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap1.5_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap1.5_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap2.0_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap2.0_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap2.5_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap2.5_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap3.0_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap3.0_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap3.5_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap3.5_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap4.0_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap4.0_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 rolling_all_sky_ns2_rw0.9_v2.0_10yrs rolling baseline_v2.0_10yrs 37 rolling_bulge_6_v2.0_10yrs rolling baseline_v2.0_10yrs 37 rolling_bulge_ns2_rw0.5_v2.0_10yrs rolling baseline_v2.0_10yrs 37 rolling_bulge_ns2_rw0.8_v2.0_10yrs rolling baseline_v2.0_10yrs 37 rolling_bulge_ns2_rw0.9_v2.0_10yrs rolling baseline_v2.0_10yrs 37 rolling_ns2_rw0.5_v2.0_10yrs rolling baseline_v2.0_10yrs 33, 37 rolling_ns2_rw0.9_v2.0_10yrs rolling baseline_v2.0_10yrs 37 62 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 8 (Continued) Simulation Name Family Comparison Baseline Included in Which Figures rolling_ns3_rw0.5_v2.0_10yrs rolling baseline_v2.0_10yrs 33, 37 rolling_ns3_rw0.9_v2.0_10yrs rolling baseline_v2.0_10yrs 35, 37 roman_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 shave_20_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_22_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_25_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_28_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_30_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_32_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_35_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_38_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_40_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 short_exp_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 six_rolling_ns6_rw0.5_v2.0_10yrs rolling baseline_v2.0_10yrs 33, 37 six_rolling_ns6_rw0.9_v2.0_10yrs rolling baseline_v2.0_10yrs 36, 37 smc_movie_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 too_rate10_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 too_rate50_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 twi_neo_brightest_repeat3_iz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_iz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_iz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_iz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_iz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_iz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_iz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_iz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_iz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_iz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 63 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 8 (Continued) Simulation Name Family Comparison Baseline Included in Which Figures twi_neo_repeat3_iz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_iz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_iz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_iz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_iz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 40, 41, 42 twi_neo_repeat4_iz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_iz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_iz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_iz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_iz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_iz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_riz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 40, 41, 42 twi_neo_repeat4_riz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_riz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_riz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_riz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_riz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_riz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 u_long_ms_30_v1.7_10yrs u_long baseline_nexp2_v1.7_10yrs 18 u_long_ms_40_v1.7_10yrs u_long baseline_nexp2_v1.7_10yrs 18 u_long_ms_50_v1.7_10yrs u_long baseline_nexp2_v1.7_10yrs 18 u_long_ms_60_v1.7_10yrs u_long baseline_nexp2_v1.7_10yrs 18 vary_expt_v2.0_10yrs vary expt baseline_v2.0_10yrs 19 vary_gp_gpfrac0.01_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.05_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.10_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.15_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.20_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.25_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.30_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.35_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.40_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.45_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.50_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.55_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.75_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac1.00_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_nes_nesfrac0.01_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.05_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.10_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.15_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.20_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 64 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 8 (Continued) Simulation Name Family Comparison Baseline Included in Which Figures vary_nes_nesfrac0.25_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.30_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.35_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.40_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.45_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.50_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.55_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.75_v2.0_10yrs vary nes baseline_v2.0_10yrs 2, 12, 13 vary_nes_nesfrac1.00_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 virgo_cluster_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 Note. A detailed summary of each of the simulations and families may be found at https://github.com/lsst-pst/survey_strategy/blob/main/fbs_1.7/SummaryInfo. ipynb (v1.5 and v1.7 simulations) or at https://github.com/lsst-pst/survey_strategy/blob/main/fbs_2.0/SummaryInfo_v2.1.ipynb (v2.0–v2.2 v simulations). (This table is available in machine-readable form.) Appendix C Metrics Values for the v2.1 Baseline Cadence Simulation Metric results for the most recent baseline survey simulation at the time of submission (baseline_v2.1_10yrs) are listed in Table 9. Table 9 Metric Values for baseline_v2.1_10yrs from the Latest Version of rubin_sim Metric Value (%) Completeness PHA H <= 6.0 93.9 Completeness PHA H <= 22.0 59.6 Completeness NEO H <= 16.0 93.0 Completeness NEO H <= 22.0 58.2 Completeness MBA H <= 16.0 100.0 Completeness MBA H <= 21.0 54.3 Completeness Jupiter Trojan H <= 14.0 100.0 Completeness Jupiter Trojan H <= 18.0 43.8 Completeness TNO H <= 6.0 69.9 Completeness TNO H <= 8.0 48.0 Completeness OCC_r5 H <= 8.0 93.8 Completeness OCC_r5 H <= 17.0 64.0 Completeness OCC_r20 H <= 8.0 85.5 Completeness OCC_r20 H <= 12.0 60.5 Completeness ‘Ayló’chaxnim H <= 16.0 0.04 Completeness ‘Ayló’chaxnim H <= 20.5 0.02 Completeness (quads) ‘Ayló’chaxnim H <= 16.0 0.17 Completeness (quads) ‘Ayló’chaxnim H <= 20.5 0.13 Fraction LC Inversion PHA H = 16.0 46.6 Fraction LC Inversion PHA H = 19.0 5.5 Fraction LC Inversion NEO H = 16.0 48.1 Fraction LC Inversion NEO H = 19.0 5.5 Fraction LC Inversion MBA H = 16.0 94.7 Fraction LC Inversion MBA H = 18.0 15.5 Fraction LC Inversion Jupiter Trojan H = 14.0 94.3 Fraction LC Inversion Jupiter Trojan H = 15.0 11.9 Fraction 4 of grizy PHA H = 16.0 84.0 Fraction 4 of grizy PHA H = 19.0 52.3 Fraction 4 of grizy NEO H = 16.0 85.4 Fraction 4 of grizy NEO H = 19.0 52.4 Fraction 4 of grizy MBA H = 16.0 100.0 65 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 9 (Continued) Metric Value (%) Fraction 4 of grizy MBA H = 18.0 89.6 Fraction 4 of grizy Jupiter Trojan H = 14.0 100.0 Fraction 4 of grizy Jupiter Trojan H = 15.0 100.0 Fraction 4 filters TNO H = 6.0 59.8 Fraction 4 filters TNO H = 7.0 41.5 Fraction 4 filters OCC_r5 H = 8.0 82.9 Fraction 4 filters OCC_r5 H = 14.0 33.4 Fraction 4 filters OCC_r20 H = 8.0 76.3 Fraction 4 filters OCC_r20 H = 11.0 38.4 Note. These values all represent the percent of the expected population that would “pass” the metric requirements. “Completeness” refers to the discovery completeness for each sample population at the indicated H value, while “Fraction LC Inversion” refers to the fraction of each population that would have observations that meet the metric requirements, implying that the object would be a good subject for light-curve inversion. Likewise for “Fraction 4 filters,” showing the fraction of each population that would be likely to obtain colors in four filters. 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Abstract

The Vera C. Rubin Observatory is expected to start the Legacy Survey of Space and Time (LSST) in early to mid-2025. This multiband wide-field synoptic survey will transform our view of the solar system, with the discovery and monitoring of over five million small bodies. The final survey strategy chosen for LSST has direct implications on the discoverability and characterization of solar system minor planets and passing interstellar objects. Creating an inventory of the solar system is one of the four main LSST science drivers. The LSST observing cadence is a complex optimization problem that must balance the priorities and needs of all the key LSST science areas. To design the best LSST survey strategy, a series of operation simulations using the Rubin Observatory scheduler have been generated to explore the various options for tuning observing parameters and prioritizations. We explore the impact of the various simulated LSST observing strategies on studying the solar system’s small body reservoirs. We examine what are the best observing scenarios and review what are the important considerations for maximizing LSST solar system science. In general, most of the LSST cadence simulations produce ±5% or less variations in our chosen key metrics, but a subset of the simulations significantly hinder science returns with much larger losses in the discovery and light-curve metrics. NASA Postdoctoral Program Fellow. LSSTC Catalyst Fellow. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. 1 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Unified Astronomy Thesaurus concepts: Trans-Neptunian objects (1705); Asteroids (72); Small Solar System bodies (1469); Near-Earth objects (1092); Comets (280); Interstellar objects (52); Sky surveys (1464); Main belt asteroids (2036) Supporting material: animations, machine-readable table 1. Introduction time is expected to be used for non-WFD observing and will likely be split between observing other portions of the sky in The Vera C. Rubin Observatory is currently under minisurveys (taking up more than a few percent of the construction on Cerro Pachón in Chile. When completed, the observing time) with different cadences, microsurveys (obser- observatory will house the 8.36 m Simonyi Survey Telescope ving strategies that require ∼1% of the observing time), and equipped with the Rubin Observatory LSST Camera 2 approximately 5% of the on-sky time dedicated to Deep (LSSTCam), which covers a 9.6 deg circular field of view Drilling Fields (DDFs; a small number of dedicated pointings (FOV). This provides the unique depth and temporal sky that will receive intensive observing at a higher cadence than coverage that will enable Rubin Observatory’s planned 10 yr the WFD). For a full description of the various components of Legacy Survey of Space and Time (LSST; Ivezić et al. 2019; the LSST and the requirements set by the SRD, readers are Bianco et al. 2022) to be an unprecedented discovery machine directed to Ivezić & the LSST Science Collaboration (2013), for solar system small bodies. With survey operations currently Ivezić et al. (2019), Bianco et al. (2022), and references therein. expected to begin in early to mid-2025, current predictions How exactly Rubin Observatory will scan the night sky is estimate that Rubin Observatory will detect over five million not fully settled. The Rubin Observatory Project and Opera- new solar system objects. In each of the solar system’s small tions teams have engaged with the wider user community to body reservoirs, an order of magnitude more objects will be optimize the LSST observing strategy in order to maximize the discovered during the LSST than cataloged to date in the Minor 37 future science returns from the resulting data set and facilitate Planet Center (MPC; Jones et al. 2009, 2018; LSST Science the best science with the survey (Bianco et al. 2022). Collaboration et al. 2009; Solontoi et al. 2010; Shannon et al. Partitioning out the non-WFD LSST observing time and fine- 2015; Grav et al. 2016; Silsbee & Tremaine 2016; Vereš & tuning the WFD observing cadence can be likened to cutting a Chesley 2017; Schwamb et al. 2018a; Ivezić et al. 2019; cake and dividing it out to attendees at a birthday party. There Fedorets et al. 2020a). In addition, the survey is expected to are many ways to cut and serve the slices of cake, but the discover at least several interstellar objects (ISOs) passing various slicing/serving options may result in very different through the solar system (Moro-Martín et al. 2009; Cook et al. outcomes. For example, cutting even slices such that everyone 2016; Engelhardt et al. 2017; Trilling et al. 2017; Seligman & gets the same portion size of cake is much more equitable and Laughlin 2018; Levine et al. 2021; Hoover et al. 2022). Beyond will likely result in a much happier crowd than cutting half the discovery, the dawn of Rubin Observatory will also usher in a cake for the first person served and dividing the other half of revolution for time-domain planetary astronomy. The LSST the cake among the rest of the attendees. LSST has four key will monitor most of its five-million-plus small body science drivers: probing dark energy and dark matter, exploring discoveries over a 10 yr period, with likely hundreds of the transient optical sky, inventorying the solar system, and observations per object split across six broadband (ugrizy) mapping the Milky Way (LSST Science Collaboration et al. filters (LSST Science Collaboration et al. 2009; Ivezić et al. 2009; Ivezić et al. 2019). What may be beneficial for one 2019). This will enable an unparalleled probe of activity within science driver in a proposed LSST observing cadence may various regions of the solar system, including cometary negatively impact the returns from another. Optimizing the outgassing/sublimation, cometary outbursts, rotational breakup LSST strategy is thus a fine balance to tune the cadence events, and asteroid collisions (Jones et al. 2009; LSST Science parameters to obtain the best science from each of the LSST’s Collaboration et al. 2009; Schwamb et al. 2018a, 2021). The key drivers while evenly distributing the “unhappiness” such large number of observations per object will also provide that no science area is overly impacted by the finalized cadence opportunities to study rotational light curves, phase curves, and decisions. photometric colors that probe the shape, size, rotation rate, and As highlighted in Bianco et al. (2022), optimizing the LSST surface composition of these small bodies (Jones et al. 2009; cadence is a multivariate problem. In order to facilitate LSST Science Collaboration et al. 2009; Schwamb et al. exploring the various options for modifying the LSST survey 2018a). The LSST will be a collection of surveys operating in strategy and the resulting impacts on the survey’s main science tandem. The main component of the LSST is the Wide–Fast– drivers, the Rubin Observatory LSST Scheduler Team has Deep (WFD), a wide-field survey covering ∼18,000 deg of developed a suite of cadence simulations (Connolly et al. 2014; the sky with a universal observing strategy. Although there is Delgado et al. 2014; LSST Science Collaboration et al. 2017; tuning to the implementation of the WFD that is possible, the Jones et al. 2020) using the Rubin Observatory scheduler main requirements for the WFD are outlined in the LSST (rubin_sim/OpSim; Naghib et al. 2019) and the Python- Science Requirements Document (SRD; Ivezić & the LSST based LSST Metrics Analysis Framework (MAF; Jones et al. 2014). The Rubin Observatory Survey Cadence Optimization Science Collaboration 2013). The SRD defines the WFD as of sky uniformly covered to a median total of Committee (SCOC) has been synthesizing the feedback from ∼18,000 deg 825 ∼30 s exposures divided across the six filters over a 10 yr the LSST user community and the output from the MAF period. Approximately 80%–90% of the LSST’s on-sky metrics to produce a formal recommendation on how to observing time will be devoted to the WFD. The remaining optimize the LSST survey strategy (Ivezić & the SCOC 2021; Bianco & the SCOC 2022). The SCOC is expected to finish its https://www.minorplanetcenter.net/ main deliberations by the end of 2023. The SCOC may request 2 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. some additional fine-tuning of the observatory strategy and are currently not explored in the cadence simulations that have recommend changes for the first year of the survey based on the the potential for hindering or enhancing solar system science with the LSST. Finally, we draw together in Section 6 knowledge gained during commissioning and benchmarking of conclusions and recommendations for tuning the LSST survey the telescope−camera system (Bianco et al. 2022). Once Rubin strategy in order to maximize solar system science and identify Observatory science operations start, it is expected that the areas for future work. Given the length of this paper, we have SCOC will periodically review the performance of the LSST included a table of acronyms and their expansions in observing cadence and subsequently recommend modifications Appendix A in Table 7. as needed. This paper is a contribution to the Astrophysical Journal 2. Simulating LSST Solar System Detections Supplement Series focus issue on Rubin LSST Survey Strategy Optimization. The focus issue aims to capture the knowledge Simulating observations of solar system objects requires learned during the process of selecting and finalizing the LSST considerations beyond those commonly used for most other initial cadence and identifying what observing strategies are or astrophysical sources. Of foremost importance are their are not suitable for each of the key LSST science areas. We nonsidereal motions and the fact that a common rest frame refer the reader to the opening paper by Bianco et al. (2022) for cannot simultaneously approximate all of them. Solar system −1 a more detailed introduction to the focus issue. The work object proper motions range from 1″ hr for distant trans- −1 presented in this paper stems from the Rubin Observatory Neptunian objects (TNOs) to 1° hr for closely approaching LSST Solar System Science Collaboration’s (SSSC) efforts to and impacting near-Earth objects (NEOs). Next, their bright- provide feedback to the SCOC. The LSST SRD does not set nesses may greatly vary depending on their orbits around the performance requirements based on detecting a certain number Sun and how closely they approach Earth. Furthermore, of solar system objects in the various small body populations. cometary activity (i.e., sublimation-driven mass loss) can Instead, the SRD outlines the minimum requirements and enhance or even dominate the intrinsic brightness of active stretch goals for the observing specifications of the LSST such objects in response to solar insolation and make them extended as single exposure depth, sky coverage, number of visits, objects. Finally, their brightnesses also vary with the phase astrometric precision, and coadded 10 yr depths that would (Sun-target-observer) angle. Other brightness variations, e.g., enable science in all four main survey science drivers. What it due to the rotational light curve, or outbursts of activity, can be means to maximize the returns on LSST Solar System science treated in ways similar to any other astrophysical source. in the context of survey cadence decisions is up for To partially illustrate the added complexities of modeling interpretation by the Rubin data rights community and the solar system objects, take, for example, a 1 km radius object in SCOC. The baseline survey strategy that was being simulated a parabolic orbit observed at solar opposition. Such an object at the start of the cadence optimization process showed an would have an apparent magnitude of order-of-magnitude increase in solar system discoveries across mH=+ () 1, 1, 0 5 log(r)+ 5 log(D)+ 2.5 log(F), 10 10 10 each of the minor planet populations (LSST Science Colla- () 1 boration et al. 2009; Vereš & Chesley 2017; Jones et al. 2018). Determining that the LSST needs to discover N objects of class where H(1, 1, 0)(or, more simply, H) is the absolute X to measure Y at the Z confidence level in order to provide the magnitude. Parameter r is the heliocentric distance in next leap forward in our understanding of the solar system is h astronomical units, Δ is the observer-target distance in extremely challenging to do. Many of the science questions that the LSST will address are not necessarily well understood (or astronomical units, and Φ is the phase function evaluated at even formulated) yet. In most cases, it is very difficult to take phase angle f. Let the 1 km object have a geometric albedo existing models of the solar system, its formation and of 4%, and then H; 17.6 mag. The apparent brightness of this evolution, and transform that into the the total number of target would range from 25th magnitude at 6 au to 17th particular kinds of objects needed and the photometric magnitude at 1.5 au from the Sun, within Rubin Observatory’s precision required to distinguish between the available models. nominal capabilities. If the 1 km object was active, the coma The analysis presented in this work is the SSSC’s attempt contribution to small-aperture photometry may be estimated as based on the collaboration’s science priorities (Schwamb et al. 2018a) to find quantitative proxies that can be calculated within mH=+ 2.5() 2-k log(r)+ 2.5 log(D)+ 2.5 log(F), cy h c 10 10 10 MAF and use these outputted metrics to identify which () 2 potential LSST observing strategies are the best and worst at enabling solar system science. where H is the cometary absolute magnitude, k is the In this paper, we review the LSST cadence simulations and heliocentric distance power-law slope for activity, and Φ is MAF metrics focusing on the impact on the detection and the phase function of the coma. Here the apparent magnitude monitoring of solar system minor planets and ISOs. In varies as Δ, rather than Δ , in order to account for the spatial Section 2, we provide an overview of how LSST moving extendedness of the coma and fixed-angular photometric object discoveries are simulated and how the relevant MAF apertures where the aperture is smaller than the apparent size metrics are calculated. Section 3 briefly describes the LSST of the coma. Let k = −4, and the comet with m = 25 mag at cadence simulations utilized in this work. In Section 4,we 6 au may brighten to m = 13 mag at 1.5 au. Move our examine the impact of various survey strategy choices and identify tension points with moving object detection and Defined as the apparent magnitude of the target as seen by the Sun at a characterization. In Section 5, we discuss additional factors that distance of 1 au (i.e., r = 1 au, Δ = 1 au, f = 0°). Ratio of the brightness at 0° phase to that of a white disk with the same https://iopscience.iop.org/journal/0067-0049/page/rubin_cadence geometric cross section. 3 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. hypothetical 1 km object to an inner-Earth orbit, and the LSST into account their motion and expected changes in brightness. As a first step, ephemerides are generated from the sample may not even observe it if survey operations never allow for orbits using OpenOrb (Granvik et al. 2009); the precise low solar elongation (low-SE) observations. Thus, solar system camera footprint is applied to determine which detections could objects have the potential to be undetected at some epochs be acquired based on their positions. Then, trailing losses and during LSST operations, saturate during others, or be missed color terms between the reference band and the observed filter altogether. are added to each record, to be used later when combined with In order to address the above challenges when simulating a (potentially modified) H value to calculate apparent observations of individual objects, a survey simulator must magnitudes for each observation. have knowledge of a target’s orbit and its activity state (e.g., We use a typical sample size of 5000 orbits per population. cometary or inactive). Furthermore, to assess a survey’s ability This generally provides enough statistical accuracy across the to detect, discover, and characterize solar system object orbital distribution to reach accuracies of a few percent at the populations, distributions of representative orbits that account 50% completeness level for discovery and characterization for the variety of orbits are also needed. Model distributions of metrics, while keeping compute requirements for a few solar system small bodies’ orbits (and their physical properties) hundred simulations reasonable. We then clone the potential are desirable. Such models are generally derived from the observations of these orbits over a range of H values (a simple known solar system populations but debiased to account for linear array, chosen with appropriate values for each individual discovery efficiencies. The survey simulator and solar system population), in order to be able to measure discovery and object orbital distributions are described in Sections 2.1 and characterization metrics across the expected range of obser- 2.2, respectively. The metrics used to analyze the simulated vable values for each population. The cloning takes place as observations are described in Section 2.3. part of metric calculation, within the MAF module of rubin_sim. At the metric calculation stage, the measured 2.1. Rubin Observatory Scheduler and Operations Simulator apparent magnitude is generated for each observation provided by the movingObjects module, taking into account each Various aspects of the current and previous iterations of the individual H value within the range used for cloning, as well as Rubin Observatory scheduler and operations simulator the effects of phase angle, distance from Earth and the Sun, (OpSim) are described in Connolly et al. (2014), Delgado trailing losses, and filter color terms. Using this apparent et al. (2014), Delgado & Reuter (2016), Yoachim et al. (2016), magnitude and the expected 5σ depth of each visit, the signal- LSST Science Collaboration et al. (2017), Jones et al. to-noise ratio (S/N) of the object in each visit is reported. In (2018, 2020), Naghib et al. (2019), and Bianco et al. (2022) addition, the probability of detection is also reported; this is and references therein. We provide a brief overview here. The close to requiring an S/N = 5 for detection but adds statistical Rubin Observatory scheduling software is part of rubin_sim scatter, which has the effect of smoothing the cutoff at 5σ, (Yoachim et al. 2022), an open-source Rubin-developed allowing occasional detection of fainter objects or occasional Python package. The rubin_sim package contains the losses of slightly brighter objects. primary LSST scheduling algorithm that will be used to The process of generating simulated small body populations choose pointings for the telescope based on real-time telemetry, is described in more detail, specifically for an NEO population, goal target maps, and configurable survey parameters. At the in Jones et al. (2018). For survey strategy evaluations, we top level, the scheduler uses a decision tree to generate include a range of sample populations from inner solar system observations in real time. The decision tree steps through the objects like NEOs, through mid-system objects like main-belt potential observing modes of (1) DDFs, (2) paired observations asteroids (MBAs) and Jovian Trojans, all the way to outer solar in a large contiguous area, (3) paired observations in twilight, system bodies like TNOs and comets. These include the and (4) single observations selected using a greedy algorithm. following: The DDFs are prescheduled for optimal times; all the other observing modes use a modified Markov decision process 1. NEOs based on a sample of orbits from Granvik et al. (MDP) similar to the one presented in Naghib et al. (2019) to (2018). A random set of 5000 orbits are drawn from the generate lists of desired observations. The MDP typically full sample of 802,000 synthetic NEOs instantiated by considers slew time, image depth, and desired footprint 41 Granvik and used for general NEO evaluation. In coverage when selecting potential observations. The schedul- addition, Earth minimum orbit intersection distance ing algorithm is paired with a model observatory to simulate (MOID) values were calculated for the full Granvik the full 10 yr LSST for these investigations. The model sample, and a subset of 5000 orbits with MOID values observatory includes a kinematic model of the telescope along <0.05 au were randomly selected to represent the with realistic weather logs, scheduled and unscheduled down- potentially hazardous asteroid (PHA) population. time, and a sky brightness model (Yoachim et al. 2016). 42 2. An ‘Ayló’chaxnim population, consisting of 10,000 Various survey strategy experiments are performed by either objects with orbits inside the orbit of Venus, was created modifying the scheduler decision tree (e.g., inserting a new via rejection sampling of the probability distribution for observing mode for taking high-airmass observations in twilight) or altering the MDP algorithm (e.g., adding a new 41 The full 802,000-object Granvik sample is available for download from basis function). https://www.mv.helsinki.fi/home/mgranvik/data/Granvik+_2018_Icarus/; the subset is selected as described in detail at https://github.com/lsst-sssc/ SSSC_test_populations_gitlfs/blob/master/MAF_TEST/granvik/Granvik% 2.2. Simulating Small Body Populations 20NEO%20Model.ipynb. Previously this population was referred to as the Vatira population or The movingObjects module in rubin_sim generates Vatiras (Greenstreet et al. 2012) before the discovery of the first known object the observations of a model small body population as the ‘Ayló’chaxnim (Bolin et al. 2020c, 2022; de la Fuente Marcos & de la Fuente objects would be seen in a particular simulated survey, taking Marcos 2020a; Greenstreet 2020; Popescu et al. 2020; Ip et al. 2022). 4 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. orbital elements given in the Granvik NEO model Table 1 (Granvik et al. 2018). Orbital elements were drawn from Rubin Colors for These Model SEDs the Granvik distribution and rejected unless they were compatible with the definition of Vatiras given in the Color (mag) S D C TNO same reference, i.e., objects between the the apocenter u − r 2.13 1.90 1.72 2.55 distance of Mercury (Q = 0.307 au) and the pericenter M g − r 0.65 0.58 0.48 0.92 distance of Venus (q = 0.718 au). Angular orbital V i − r −0.19 −0.21 −0.11 −0.38 elements not provided through the Granvik model were z − r −0.14 −0.30 −0.12 −0.59 y − r −0.14 −0.39 −0.12 −0.70 sampled from uniform distributions. To achieve reason- able statistical signal, this population is simulated with a Note. LSST catalogs will contain measurements reported as “top-of- larger sample size, as each individual orbit is inherently atmosphere” AB magnitudes. unlikely to be observed. 3. MBAs and Jupiter Trojans, based on a random sample of 5000 MBAs and 5000 Jupiter Trojan asteroids (respec- tively) from the Panoramic Survey Telescope and Rapid 1. The rough positions of the object at each night Response System (Pan-STARRS) Synthetic Solar System throughout the survey lifetime are calculated using Model (S3M; Grav et al. 2011). OpenOrb. 4. TNOs, based on a random sample of 5000 objects from 2. If the rough positions are within a tolerance value of any the L7 model from CFEPS (Canada–France Ecliptic visit in a simulated survey, a more precise position at the Plane Survey; Petit et al. 2011). time of that visit is calculated, along with the expected V- 5. Oort Cloud comet (OCC) populations, created from the band magnitude as calculated by OpenOrb for the H long-period comet model of Vokrouhlický et al. (2019). value recorded with the database (typically a fiducial Two different samples of 5000 comets are created, one placeholder value of H = 20 mag). with a maximum perihelion distance of 5 au and another 3. If the position at that time lands within the camera with a maximum perihelion distance of 20 au. footprint aligned with the boresight and rotation angle of For the comet populations, we include a cometary brightening the visit, the position is recorded as a potential function, based on the Afρ quantity of A’Hearn et al. (1984), observation. using a translation from H to cometary nuclei radii, and 4. The trailing loss and color term for that particular visit are parameters appropriate for long-period comets. Intrinsic light recorded (depending on the seeing of the visit, velocity of curves due to variations in shape of the objects or surface the object, the color of the object, and the filter used for albedo or color variations are not included for any population the visit). Solar system objects will be moving during but would be useful to include in the future. These populations LSSTCam exposures. Depending on the object’s velocity do not include every population across the solar system but and the observation’s exposure time, a solar system serve as a representative sample covering a wide range of object’s point-spread function (PSF) can appear extended apparent velocities, sky coverage, and orbital parameters for the along the direction of motion. Compared to a point source purposes of evaluating the impacts of changes in survey of the same apparent magnitude, a trailed source will strategy. have a lower S/N because the photons are spread across A variety of solar system reflectance spectra are assigned to more pixels on the detector. As a result, the Rubin the members of these populations, in order to determine color Observatory’s detection algorithm is not as sensitive to terms for the LSST filters. The general simple rule of thumb is trailed sources. The algorithm uses a stellar PSF-like that Bus-DeMeo (DeMeo et al. 2009) spectral energy matched filter to find sources in the LSST images that are distributions (SEDs) are assigned to objects depending on at or above the 5σ S/N detection limit. The trailing loss their semimajor axes; orbits with semimajor axes smaller than calculated in this step accounts for both the decrease in 2 au are assigned to S types, orbits with semimajor axes larger S/N and drop in detection efficiency compared to than 4 au are assigned to C types, and orbits between 2 and 4 au stationary point sources. We refer the reader to Section are assigned randomly to S versus C with a linear increase in 5.1.4 of Jones et al. (2018) for further details. probability as a function of semimajor axis, in accordance with 5. The series of potential observations are evaluated for an Ivezić et al. (2001). This means that ‘Ayló’chaxnims are array of H values. For example, the NEO population is entirely S type, the Trojans are entirely C types, while PHAs, evaluated for H values ranging from 16 to 28 mag, at NEOs, and MBAs are a mix of S and C types. The TNOs are steps of 0.2 mag. At H = 16 mag, the apparent magnitude assigned a significantly redder, TNO-specific SED, appropriate of the object that will be measured by the Rubin for the typical colors of a red dynamically excited TNO or a Observatory source detection pipeline in each visit is bluer object from the red cold classicals. The OCC populations calculated by combining the ephemeris V magnitude, the are assigned D-type SEDs, as a reasonable approximation for trailing losses, the color terms, and an offset between the the colors of the cometary nuclei. Colors for these populations fiducial H value and the current “clone” value of H = 16 are shown in Table 1. In reality, objects in each of these mag (for cometary populations, there is also a calculation populations show a range of colors, so this is a simplification of the cometary brightening).At H = 22 mag, the same but is sufficient for survey strategy evaluation purposes, as the process is repeated, but more of the potential observations same H-orbit-color distributions are applied to all the LSST of the object will fall below the 5σ S/N limit, so fewer cadence simulations used in this work. To illustrate this process more concretely, for each orbit in observations will be considered (and at a lower S/N) for the test population: the calculation of each metric for each object. 5 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 2 Key Solar System MAF Metrics Used in This Analysis Population Main Metrics Discovery Metrics a,b ‘Ayló’chaxnims 3 nightly pairs in 15 nights discovery completeness for H „ 16.0 3 nightly pairs in 15 nights discovery completeness for H „ 20.5 4 detections in 1 night discovery completeness for H „ 16.0 4 detections in 1 night discovery completeness for H „ 20.5 PHAs 3 nightly pairs in 15 nights discovery completeness for H „ 16.0 3 nightly pairs in 15 nights discovery completeness for H „ 22.0 NEOs 3 nightly pairs in 15 nights discovery completeness for H „ 16.0 3 nightly pairs in 15 nights discovery completeness for H „ 22.0 MBAs 3 nightly pairs in 15 nights discovery completeness for H „ 16.0 3 nightly pairs in 15 nights discovery completeness for H „ 21.0 Jupiter Trojans 3 nightly pairs in 15 nights discovery completeness for H „ 14.0 3 nightly pairs in 15 nights discovery completeness for H „ 18.0 TNOs 3 nightly pairs in 15 nights discovery completeness for H „ 6.0 3 nightly pairs in 15 nights discovery completeness for H „ 8.0 OCCs with q „ 5 au 3 nightly pairs in 15 nights discovery completeness for H „ 8.0 3 nightly pairs in 15 nights discovery completeness for H „ 17.0 OCCs with q „ 20 au 3 nightly pairs in 15 nights discovery completeness for H „ 8.0 3 nightly pairs in 15 nights discovery completeness for H „ 12.0 Light-curve metrics PHAs Fraction of H = 16.0 with sufficient observations for light-curve inversion Fraction of H = 19.0 with sufficient observations for light-curve inversion NEOs Fraction of H = 16.0 with sufficient observations for light-curve inversion Fraction of H = 19.0 with sufficient observations for light-curve inversion MBAs Fraction of H = 16.0 with sufficient observations for light-curve inversion Fraction of H = 18.0 with sufficient observations for light-curve inversion Jupiter Trojans Fraction of H = 14.0 with sufficient observations for light-curve inversion Fraction of H = 15.0 with sufficient observations for light-curve inversion Notes. In the figures presented in this work, these metrics are normalized and compared to the baseline simulation for a range of cadence simulation families by varying a different survey strategy parameter. Previously referred to in the literature as Vatiras. Metrics for the ‘Ayló’chaxnims are only analyzed for simulation families that include low-SE twilight observations. Only assessed for simulations that take four observations per pointing during twilight. Metrics for OCCs are only calculated since the v2.0 simulations. 6. The result is a series of values for each metric, Generally speaking, our current solar system science metrics corresponding to the combination of the positions can be split into two categories: discovery metrics and resulting from each orbit with the apparent magnitudes characterization metrics. Discovery metrics relate to which resulting from a range of H values. objects could be discovered in the survey, while characteriza- tion metrics cover a broad range of science areas such as This is repeated over all of the orbits in the test population. likelihood of detecting activity on the surface of an object or likelihood of acquiring a color measurement. For each metric, the value per orbit−H magnitude combination is calculated and 2.3. LSST Solar System Science Metrics recorded, and then a “summary value” is evaluated across the With the movingObjects and MAF modules of rubin_- entire population. For discovery metrics, this summary value is sim, the calculation of any arbitrary quantity per object is the fraction of the population that can be linked by Rubin straightforward. The MAF software identifies the observations Observatory’s Solar System Processing (SSP) pipelines (Myers of a given object (or more specifically, orbit and H value, in the et al. 2013; Jurić et al. 2020)—the discovery completeness. For case of cloning) and passes these to the MAF Metric, where characterization metrics, the summary value is typically the the value can be calculated based on the acquired observations fraction of the population that meets a given threshold—the and then saved. Summary values across the entire population, fraction of the population that is likely to meet light-curve such as fraction of objects with light-curve inversion potential inversion requirements, for example—although it can also be or “discoverable” objects, can be calculated from the results. the mean or median or maximum (etc.) value of the metric 6 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 3 Secondary Solar System MAF Metrics Used in This Analysis Population Secondary Metrics Color Light-curve Metrics PHAs Fraction of H = 16.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy Fraction of H = 19.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy NEOs Fraction of H = 16.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy Fraction of H = 19.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy MBAs Fraction of H = 16.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy Fraction of H = 18.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy Jupiter Trojans Fraction of H = 14.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy Fraction of H = 15.0 with the equivalent of 40 S/N = 5 detections or 10 S/N = 20 detections per filter in grizy TNOs Fraction of H = 6.0 with at least 30 S/N > 5 observations in 1 filter and 20 observations in 3 other filters Fraction of H = 8.0 with at least 30 S/N > 5 observations in 1 filter and 20 observations in 3 other filters OCCs with q „ 5 au Fraction of H = 8.0 with at least 30 S/N > 5 observations in 1 filter and 20 observations in 3 other filters Fraction of H = 17.0 with at least 30 S/N > 5 observations in 1 filter and 20 observations in 3 other filters OCCs with q „ 20 au Fraction of H = 8.0 with at least 30 S/N > 5 observations in 1 filter and 20 observations in 3 other filters Fraction of H = 12.0 with at least 30 S/N > 5 observations in 1 filter and 20 observations in 3 other filters Notes. In the figures presented in this work, these metrics are normalized and compared to the baseline simulation for a range of cadence simulation families by varying a different survey strategy parameter. Metrics for OCCs are only calculated since the v2.0 simulations. across the population. These summary values are reported at each pair; in the default configuration, the minimum time either a particular H value or cumulatively, for objects with H separation was set to 0 minutes, and the maximum time less than or equal to a given H value. These summary values at separation was set to 90 minutes, corresponding to the both a bright H (large size) and a fainter H (smaller size) are approximate limits suggested by early expectations for pulled out for each population for comparison of multiple configuration for the solar system processing pipelines and simulations. The particular H values used are dependent on the very widely bracketing the typical expected separation of visits. population; typically the bright H value is where the metric The overwhelming majority of visits in the survey are acquired results reach their highest value and then remain constant with in pairs with a separation of 22–30 minutes (depending on the decreasing H. The fainter H values are typically set close to details of the survey configuration); the pairs of visits are where the baseline survey strategy reaches about 50% for that usually acquired in “adjoining” filters (i.e., g and r or r and i metric result. The discovery and characterization metrics used visits for a pair); and, coupled with the large FOV of Rubin, in this paper are listed in Tables 2 and 3. The details of how most although not all observations of an object are followed up these metrics are calculated are described below in by a second observation in the same night. It is also helpful to Sections 2.3.1–2.3.3. consider objects that could be discovered via more traditional methods of identifying four observations on the same night 2.3.1. Discovery Metrics (i.e., “quad detections”). This is particularly useful when considering observations of near- or interior-to-Earth asteroids The SSP pipelines (Myers et al. 2013; Jurić et al. 2020) will within the special near-Sun twilight microsurvey, where link transient sources from the nightly visits into “tracklets” observations are purposefully obtained in quads in order to (potential linkages in the same night using linear extrapolation). secure identifications of these rapidly disappearing asteroids. If SSP will identify new moving objects by attempting to link the observations of a given orbit−H combination meet the together three tracklets from within a 15-day window onto a required criteria at least once, the object is considered heliocentric orbit. The current baseline LSST object discovery “discovered”; to compare the results across different simula- guidelines require pairs of observations on three separate tions of survey strategy, the discovery completeness is reported nights, within a window of 15 days as the design goal and 30 at both a bright and faint H value for each population. More days as the stretch goal; the 15-day requirement is a confident details about the discovery metrics are presented in Jones et al. lower limit, but a 30-day window is a reasonable extension that (2018), including more background about the potential for is also useful to consider. Thus, the basic discovery metric false-positive discoveries. In short, we do not expect a searches for precisely this: pairs of observations on at least significant number of false-positive detections, regardless of three different nights within 15 or 30 days, using the survey strategy choices, with the criteria of three pairs of probabilistic detection value to determine what is visible or detections over a window of either 15 or 30 nights; this is due not. The metric allows for setting the minimum and maximum time separation between the individual visits in The minimum time separation for pairs of visits was set to 0 minutes during the metric runs analyzed in this paper. In the future, we will be using 5 minutes The probabilistic detection likelihood depends on the expected 5σ point- as the minimum separation time. However, we do not anticipate there being a source depth, determined by sky brightness, air mass, and seeing alone; it does significant drop in metric performance, as the overwhelming number of pairs of not take into account potential crowding in the field. visits are acquired at very close to the goal time separation, around 33 minutes. 7 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. to a range of factors, including the low fraction of false-positive asteroid from photometric measurements over a wide range of detections coming from difference imaging and the low viewing geometries as suggested in LSST Science Collabora- likelihood of pairs of detections on three separate nights tion et al. (2009; e.g., Hanuš et al. 2011; Ďurech et al. 2016; aligning within expected residuals for initial orbit Muinonen et al. 2020). The light-curve inversion metric determination. evaluates the suitability of a set of observations for this The various survey strategy simulations and populations process. The evaluation is based on the phase curve and ecliptic expose some basic trends: longitude coverage provided by the observations, as well as the overall number and S/N of each observation, considering 1. Discovery completeness for slow-moving populations, observations in a single filter at a time. The ecliptic such as TNOs, depends strongly on the total area longitude range of the observations must be more than 90° included in the survey. Because these objects move so ecliptic longitude and cover more than 5° of phase angle, as a slowly year over year and are relatively “easy” to proxy of the required range of viewing geometries. Further, discover via linking, the footprint itself is the most there must be more than a threshold value of S/N-weighted important consideration of the survey strategy, particu- observations, equivalent to about 50 S/N = 100 observations larly for the brighter TNOs. The completeness for the or 250 S/N = 20 observations, all in the same filter, in order to fainter TNOs can also vary slightly depending on which provide enough photometric measurements. Like all other filters are paired together in visits and whether the most metrics within MAF, the rotation of the asteroid and its impact sensitive filters are used often enough within the window. on the photometric measurements is not considered; presum- 2. Discovery completeness for fast-moving populations, ably this would be part of the light-curve inversion process. If such as NEOs, depends more strongly on the number all conditions are met, then light-curve inversion is at least of visits per pointing. Since NEOs travel across much potentially likely; thus, this metric provides a likely upper limit more of the sky on the timescale of the survey, the on the fraction of the population for which light-curve footprint is not as much of a constraint as for TNOs. inversion may be possible. This is evaluated per orbit−H However, the total number of visits in the survey is combination, and then the fraction of the population (at a bright relatively constant with different survey strategies, and so and fainter H value) is reported. Outer solar system objects the footprint influences the number of visits per pointing never achieve the required range of viewing geometries, and and thus the typical cadence of those visits. Fainter NEOs objects where the nucleus is shrouded with coma such as active in particular may only be visible for a short period of comets are also not good candidates; this metric is not time; thus, more visits per pointing result in a higher evaluated for these populations. likelihood of an object having observations suitable for This metric is very sensitive to the number of observations discovery, and so a higher discovery completeness. For per pointing, but also to the cadence of those observations. the brightest NEOs, the footprint weighs in as well, as Generally, we find a trend across the simulations that the light- covering more sky results in discovering more NEOs. curve inversion results track in a similar sense for all of the 3. Intermediate populations, such as MBAs, fall in between inner solar system populations, with NEOs being least sensitive these extremes. In general, we find a threshold number of to survey strategy variations, followed by PHAs, then MBAs, visits per pointing results in good completeness for a and finally Trojan asteroids showing the most variation in given population, and this threshold increases as the H metric results as survey strategies change. value being evaluated gets larger and/or the population includes more small semimajor axis or high-inclination or high-eccentricity orbits. 2.3.3. Color Light-curve Metrics 4. The Jupiter Trojans show stronger variability with some There are several metrics relating to determining colors for kinds of survey strategy changes that include changes in the small body population members, tailored for inner solar the timing of observations. Some survey strategies focus system or outer solar system objects. As the LSST will not visits on particular regions of the sky in particular years, obtain instantaneous colors, each of these metrics also includes such as in the rolling cadence. These variations can result some requirement on measuring a light curve. in a higher or lower sampling of the Jupiter Trojan For the inner solar system, the color light-curve metric population depending on the timing of visits, as these evaluates the number of S/N-weighted observations per asteroids are both more spatially constrained and moving bandpass to evaluate whether the color could be determined across the sky. in that bandpass. Essentially, this could be translated to fitting 5. More relaxed discovery criteria result in more discov- the light curve in each bandpass alone and then combining eries, but with similar trends. For example, 30-day these light curves to evaluate the color. The equivalent of windows perform about 2%–5% better than 15-day 40 S/N = 5 detections or 10 S/N = 20 detections per filter are windows for fainter objects, depending on the population required, but the more extensive requirements that relate to (brighter objects show little difference). However, these achieving a range of viewing geometries for light-curve different criteria follow similar trends between survey inversion are not, and no limitation is set on when the strategies, meaning that evaluating 15-day windows observations are acquired. The specific number of detections shows similar preferences in survey strategy to evaluating needed is based on an estimate of the amount of data that would 30-day windows. be sufficient to measure basic light-curve, color, and phase- curve parameters with scientifically meaningful uncertainties. Although work is still needed to use the sparse LSST-like 2.3.2. Light-curve Metrics cadence to determine these parameters, a preliminary assess- Inner solar system objects have the potential to be subjects ment suggests that 20–40 observations per color should be for sparse light-curve inversion, inferring the shape of the sufficient. While phase curves are also necessary for this 8 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. analysis, we elected to keep the metric simple by not requiring No ISOs were simulated for this work. With only two ISOs a particular spread in phase angles. In practice, almost any known to date (Meech et al. 2017; Borisov 2019), the cadence will produce sufficient constraint on the phase curve to characteristics of this population are currently unconstrained. allow for colors to be determined for the vast majority of Long-period comets are distributed across the sky with a much objects. The summary values reported are the fraction of the larger range of ecliptic latitudes compared to the MBAs and population (at a given H value) for which two specific colors TNOs, due to the effects of passing stars and Galactic tides that (g − r or g − i plus g − z or g − y), three specific colors (g − r, shape the Oort Cloud into a shell rather than a flared disk shape r − i, i − z or r − i, i − z, z − y), four colors (g − r, r − i, i − z, (Everhart 1967; Fernández 1997; Francis 2005; Higuchi et al. z − y), or all five colors (adding u − g to the four-color set) are 2007; Brasser et al. 2010; Dones et al. 2015; Vokrouhlický potentially determined. et al. 2019; Higuchi 2020, and references therein). Recent For the outer solar system, a slightly different color light- predictions by Engelhardt et al. (2017), Seligman & Laughlin curve metric evaluates the number of observations reaching a (2018), and Hoover et al. (2022) suggest that LSST ISO minimum threshold (S/N ≈ 5). This metric requires at least 30 discoveries will cover a wide range of ecliptic latitudes and observations in a “primary” bandpass and then 20 observations heliocentric distances similar to long-period comets. Thus, we in the additional bandpass(es). This is equivalent to assuming a assume that the trends seen for the simulated LSST OCC light-curve fit in the primary bandpass with additional discoveries can provide some broad guidance for how the observations in the secondary bandpass serving to help fit the cadence decisions will impact LSST ISO discoveries. Like 1I/ light curve and color, possibly simultaneously (such as would ‘Oumuamua, which was discovered at 0.22 au (Meech et al. be possible with multiband Lomb–Scargle fitting). The 2017) moving at 6°.2 per day, a subset of ISOs discovered close summary values reported are the fraction of the population to Earth will on short timescales (10 days) look similar to (at a given H value) for which one, two, or more colors can be NEOs (e.g., Cook et al. 2016). Therefore, the NEO metrics are fitted, without restrictions on which bandpasses are used. also insightful for gauging the potential impacts to the ISO discovery rate. The solar system MAF metrics assume equal detection 2.3.4. Metric Limitations efficiency across all areas of the survey footprint (even near the plane of the Galaxy, where stellar crowding may be a factor). As described above in Section 2.2, the most accessible and Rubin Observatory’s data pipelines will detect solar system up-to-date orbital and absolute magnitude distributions have bodies using difference imaging. Templates representing the been used to model the expected LSST solar system detections. static sky will be subtracted from the nightly images, and what The physical and orbital properties of the modeled synthetic remains will be a variable, transient, or moving source. This small bodies are driven by observational data, but the LSST will help significantly in detecting solar system objects in cadence simulations do have to make some assumptions about regions of high stellar density, but stellar crowding will likely these small body populations. This is particularly true on the cause some decrease in the efficiency of Rubin Observatory’s smallest size scales that have not been very well probed by past difference image analysis (DIA) and SSP pipelines. The MAF wide-field surveys. The distribution of different surface types solar system metrics are likely overly optimistic near and in the applied to the various simulated small body reservoirs will also Galactic plane, where stellar crowding is the highest. This impact the apparent magnitude of the synthetic objects in the should be kept in mind when examining the cadence various optical filters. Additionally, we have to make simulations modifying the LSST Galactic plane observing simplifying assumptions about active objects. We assume that strategy. all comets will generate dust coma with the same relation The Rubin scheduler aims to take image pairs, each night per applied to calculate the observed apparent magnitude, and the pointing, to facilitate the identification of moving solar system effects of cometary outbursts are ignored. In addition, rotational objects (Ivezić & the LSST Science Collaboration 2013). The brightness variations due to shape or uneven surface albedo are time between these repeat observations is a tunable survey not accounted for in these simulations. Thus, the exact number parameter. The Rubin SSP pipelines (Myers et al. 2013; Jurić of solar system minor planets found by LSST will differ from et al. 2020) require motion within a single night for initial that “discovered” in the simulations explored in this paper discovery. Transient sources that appear stationary between the because of these choices. two images taken on the same night will not be included in the The smaller solar system minor planet populations, such as daily tracklets that the SSP algorithm will try to link with the main-belt comets (MBCs), Jupiter-family comets (JFCs), tracklets from previous nights. The Rubin SSP pipelines as sungrazing comets, Neptune Trojans, and Centaurs, have not currently planned will not be able to detectbodies beyond been simulated for this work. For the MBCs, sungrazing ∼100–150 au (see Section 4.4.1 for a detailed estimate), but comets, and JFCs this is partly due to having to develop a other search algorithms will likely be developed by the wider representative activity model. We can instead use the community to search for very slow moving objects in the LSST populations that are simulated in the rubin_sim simulations data. The MAF solar system discovery metric does not account as proxies to help inform what the impact of various cadences for SSP’s slow motion limit. The only metrics really impacted might be. Simulated survey strategies that will improve the by this are the estimated TNO discoveries. As long as the metrics for MBAs and NEOs will also likely enhance the median separation between the observations is similar for a set discovery and monitoring of MBCs and JFCs. Cadences that of cadence simulations, then the output from the discovery improve the chances of finding near-Sun ‘Ayló’chaxnims will likely increase the LSST discovery rate of sungrazing comets, metrics can be compared. We note that some care must be like the Kruetz family. As the Centaurs reside in the middle taken when considering the impact of varying the time solar system, the impacts on the Centaurs can be extrapolated separation between repeat observations, and we refer the using the TNO and MBA simulation metrics. reader to Section 4.4.1 for further discussion. 9 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. There are currently no MAF metrics that measure how runs were redone with an update in the scheduler configuration precise small body orbital predictions and characterization will after the submission of this paper owing to an issue with the be based on Rubin Observatory observations. The accuracy of sky distribution of u-band observations. We use the updated the orbits of moving objects is primarily driven by the v2.2 simulations in our analysis. The v1.5−v2.2 cadence observational arc length. There were no reasons to consider simulations are described in detail in Jones et al. (2020) and the observational arc length separately with a dedicated MAF Yoachim (2022). Short descriptions of the simulations are also metric because all of the observing strategy options currently available in online Jupyter notebooks. The resulting MAF being considered as part of the LSST cadence optimization metrics derived from these simulations are available in online exercise (see Section 3) have repeat coverage of the entire CSV (comma-separated values) files. LSST footprint over several years. This should be sufficient for We focus in this paper on the key survey strategy parameters the needs of the majority of astrometric and dynamical solar that drive significant changes in the detectability and system science cases. We also note that if a cadence option not characterization of solar system objects or would lead to covering the entire sky over the majority of the 10 yr time span unique planetary astronomy data sets that only Rubin were evaluated, it would be undesirable for other science cases Observatory could provide. Several of the simulation families such as proper-motion measurements. were repeated in later versions with improvements to the Rubin The likelihood of having satellite streaks and glints present scheduler, changes to the prescription used in the scheduler, or in LSST images is increasing with every satellite constellation modifications to the planned survey footprint. For this work, if launch (e.g., Starlink, Project Kuiper, and OneWeb). The a simulation family was repeated in later releases, we only impact of future satellite constellations is not currently taken review the latest version. We also note that the OCC orbital into account by the metrics. We discuss the potential impacts of distributions were only incorporated as MAF metrics in release the ongoing industrialization of the near-Earth environment in 2.0 and onward. We include the OCC metrics when available. Section 5.4. The v2.1 simulations include a range of families that explore Keeping these caveats in mind, the LSST cadence simula- the final details of the DDF observing strategy. No solar system tions and the MAF metrics can be used to explore the impact of metrics were run against these v2.1 DDF families, as very small various changes to the LSST observing strategy. Some care is numbers of solar system objects will be discovered in these required in examining certain families of simulations. Overall, fields compared to the rest of the survey footprint as a result of by adopting the same synthetic small body populations for each the fact that the DDFs take 5% of the observing time at of the cadence simulations and focusing on the relative change locations high off the ecliptic. The main lever arm for solar in the MAF metrics compared to the baseline survey, we can system science in relation to the DDFs is the fraction of total still gain a good understanding of the impact caused by tuning observing time spent on the DDFs, which is explored in various LSST observing parameters. Section 4.6. Simulations covering rotational and positional dithers between repeat survey pointings are also not explored 3. Overview of the LSST Cadence Simulations here because of the negligible impact on the solar system (Versions 1.5–2.2) metrics. Over the past several years, a variety of LSST cadence Appendix B (Table 8) gives a brief overview of the LSST simulations have been generated (e.g., LSST Science Colla- cadence simulations evaluated in this paper. The LSST cadence boration et al. 2009, 2017; Ivezić et al. 2019; Jones et al. 2020; simulations can be divided into several broad categories or Yoachim 2022) exploring various avenues for optimizing the families exploring different modifications to the survey WFD survey and exploring different scenarios for what to do footprint, filter distribution, intranight visits, DDF observing with the remaining ∼10%−20% of survey time. We examine strategy, visit exposure times, rolling cadence strategies, and the LSST cadence simulations produced after the implementa- microsurveys. Each simulation family explores changing one tion of the Feature Based Scheduler system (Naghib et al. parameter in the LSST observing strategy. The footprint 2019), as this iteration of the Rubin scheduler is closest to the families explore the shape and location of the WFD on-sky version that will be in place during survey operations, starting footprint, as well as the possible adoption of a variety of with the version 1.5 simulation release. At the time of this minisurveys, strategies surveying the sky outside the WFD paper’s submission, additional families of simulations have footprint or with a different cadence to the WFD that require a been released up to version 2.2. The v1.5 simulations were few percent or more of the total available LSST observing time. released in 2020 May, version 1.6 in 2020 August, v1.7 in One such example of a minisurvey is observing the northern 2021 January, and v1.7.1 in 2021 April. These simulations ecliptic region. Microsurveys are small observing campaigns cover a wide range of variations of the survey strategy that requesting ∼0.3%–3% of the total observing budget. Rolling informed the first round of the SCOC’s review. After assessing cadence in this context focuses on prioritizing observing some the feedback from the Rubin user community, the SCOC parts of the sky over others in order to acquire more recommended a new round of simulations (v2.0) to inform their photometric data points in a given observing season. This final deliberations (Ivezić & the SCOC 2021; Bianco et al. enables faster and better identification of supernovae, kilo- 2022). The v2.0 cadence simulations were made available in novae, and other astrophysical transients (LSST Science 2021 November. Two additional smaller sets of simulations Collaboration et al. 2017; Yoachim 2021). were released in 2022 April and June (v2.1 and v2.2) that clarify/explore some limited options identified after commu- https://github.com/lsst-pst/survey_strategy/blob/main/fbs_1.7/ nity review of the 2.0 cadence simulations, including DDF SummaryInfo.ipynb and https://github.com/lsst-pst/survey_strategy/blob/ observing options, new parameters for implementing the main/fbs_2.0/SummaryInfo_v2.1.ipynb. twilight low-SE solar system observations, and revised https://github.com/lsst-pst/survey_strategy/tree/main/fbs_1.7 and https:// scenarios for Galactic plane observing. The v2.2 simulation github.com/lsst-pst/survey_strategy/tree/main/fbs_2.0. 10 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 1. Metric values for the primary metrics under consideration for baseline_v2.1_10yrs from the latest version of rubin_sim. These values represent the fraction of the simulated population that would “pass” the metric requirements. “Completeness” refers to the discovery completeness for each sample population at the indicated H value, while “Fraction LC Inversion” refers to the fraction of each population that would have observations that meet the metric requirements, implying that that object would be a good subject for light-curve inversion. Likewise for “Fraction 4 filters,” showing the fraction of each population that would be likely to obtain colors in four filters. Full descriptions of the metrics are listed in Tables 2 and 3. Numerical values are provided in Appendix C. 4. Impact of Survey Strategy Choices in Table 2. We focus our analysis on the discovery metric that best matches the SSP discovery requirements (three tracklets How to evaluate whether a specific LSST survey strategy is detected within 15 nights), as other variations of the discovery “good” or “bad” for solar system science has a complex metrics require bespoke community-developed software tools. answer. How does one weigh a significant improvement in In a small number of instances reviewing the color light-curve NEO discoveries to a large loss in the number of TNOs found? metrics calculated for four colors was also useful for It depends on which population one is interested in studying interpretation (see Table 3 for input parameters), but we will and on the science goals one wants to achieve. We choose a primarily focus on the discovery and light-curve inversions for unified approach when evaluating the various LSST cadence this work. When examining a given cadence experiment, we simulations. We equally consider the impact on the main solar normalize all the metric values calculated to the relevant system populations probed by LSST: NEOs, PHAs, TNOs, baseline cadence or reference simulation that we consider the MBAs, ISOs, and OCCs. Secondary consideration is given to default scheduler parameter setting or configuration for this the smaller populations such as giant planet Trojans and inner- cadence experiment. See Figure 4 for an example where the Earth objects (IEOs; objects on orbits interior to Earth’s orbit). resulting solar system metrics for discovery (top) and light- Although an ISO population is not simulated for this work, we curve inversion (bottom) are presented. We note that the Jupiter use the OCC and NEO metrics where appropriate to examine Trojans have the most variable metrics owing to their smaller the impact on the ISOs in the various cadence simulations (see numbers and constrained positions on the sky. Metric results Section 2.3.4). The SSSC Science Roadmap (Schwamb et al. for the most recent baseline survey simulation at the time of 2018a) sets out the collaboration’s science priorities with LSST submission (baseline_v2.1_10yrs) are shown in data. The document was designed specifically to guide future Figure 1 and listed in Appendix C (Table 9). cadence decisions and ranks the key solar system research We deem reductions in the relevant metrics larger than ∼5% themes for investigation with LSST. Based on the SSSC unsuitable. The small body science goals set out in the SSSC Science Roadmap, for each LSST cadence simulation we Science Roadmap (Schwamb et al. 2018a) are derived from evaluate in priority order the impact on (1) discovery/orbital increasing sample sizes by an order of magnitude. This ∼5% characterization, (2) color measurements, and (3) rotational threshold prevents a “death by a thousand cuts” scenario where light curves. all the tuned cadence parameters produce individually small We have found that per small body population the light- impacts on the metrics but when combined cause a significant curve inversion and discovery metrics sufficiently encapsulate reduction in solar system science. This constraint also buffers the requirements for obtaining reliable broadband colors, such against any future unexpected small observing time losses. We that the majority of cadence simulation families are evaluated have provided written feedback to the SCOC identifying which using these two metrics alone. The main metrics used in our cadence simulations pass or fail our criteria (are “good” or analysis and the parameters used in their calculation are listed “bad” for solar system science). In this paper, we will not 11 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 2. The total number of visits in all filters after 10 yr for a representative sample of LSST simulations. In several of these scenarios the effective on-sky footprint of the WFD survey and other observing areas, including the NES, Galactic plane (GP), and south celestial pole (SCP) regions, change depending on how the observing time on-sky is divided. The DDFs are also visible as a collection of single fields receiving a higher number of observations than a WFD pointing, with each DDF receiving approximately 1% of the total LSST observing time. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. identify every simulation that fails our thresholds, as this can be 4.1. Survey Footprint readily identified using the relevant figures within the following The LSST footprint determines what sky is observed over sections and the MAF output. Instead, we focus on examining the 10 yr survey and how the total number of on-sky visits gets the trends in the solar system metrics as each knob is turned apportioned across the major components of the LSST. and providing recommendations based on this analysis. 12 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 3. The footprint of the NES in equatorial coordinates. The light-blue shading represents the pointings requested as part of the NES. The solid black line represents the ecliptic. The dashed black lines represent ±10° ecliptic latitude. The solid blue line plots the center of the Galactic plane. The dashed blue lines mark ±10° Galactic latitude. The plot is centered on α = 0 and δ = 0. R.A. is marked every 30°, and decl. lines are visible every 15° up to and including ±75°. Examples can be seen in Figure 2, which depicts a solar system populations like the MBAs will complete full representative set of footprints explored in the v2.0–v2.1 orbits. This means that MBAs in the northern hemisphere at the simulations. In this section, we focus on the arguments for start of the survey will be in favorable positions to be detected incorporating the northern ecliptic region into the LSST with within the WFD during the later years of the survey. This is not the Northern Ecliptic Spur (NES) minisurvey. We also examine true for outer solar system objects whose orbital periods are the amount of observing time that should be divided between well beyond ∼10 yr. Outer solar system bodies will only have a the Galactic plane and NES minisurveys and options for the small fraction of their orbital periods covered by the LSST. shape and extent of the WFD footprint. Later sections will Thus, the vast majority of TNOs located in the NES at the start discuss variations on how these visits are executed, such as of the survey will remain in the northern hemisphere, missing how they are distributed over time (Sections 4.4 and 4.5) and the WFD footprint. This is reflected in Figure 4, where the first by filter (Section 4.3). Small modifications to the footprint two simulations plotted are baseline_v1.5_10yrs, which using much less than a few percent of the observing time are includes the NES minisurvey, and filterdist_indx2_- presented in the microsurvey discussion in Section 4.7. v1.5_10yrs, which excludes the NES. TNO discoveries suffer nearly a 30% loss with the exclusion of the NES minisurvey, while there is only a very small drop for the inner 4.1.1. Northern Ecliptic Spur solar system populations. Although not simulated at the time in The WFD by its design requirements is meant to cover the this cadence experiment, populations that are more uniformly majority of the sky in the southern celestial hemisphere below distributed on the sky (such as OCCs and ISOs) also benefit 0° decl. (Ivezić & the LSST Science Collaboration 2013), but from the inclusion of the NES, which provides additional sky the Simonyi Survey Telescope is capable of observing the coverage and therefore more chances for discovery. entire ecliptic. The extent of the WFD has evolved over time Figure 4 also shows that the light-curve metrics for small (as shown in Figure 2 and later discussed in Section 4.1.2), but MBAs suffer a bit more than a 15% loss when the NES no matter what the proposed variations to the WFD sky minisurvey is not executed. Discovery relies on the object coverage are, the full extent of the ecliptic plane will not be being above the 5σ limiting magnitude on three nights, but to incorporated into the WFD footprint. The NES minisurvey perform light-curve inversion requires many more observa- aims to remedy this situation by ensuring that higher-airmass tions. The NES provides additional opportunities to observe observations of the northern ecliptic are taken as part of the those faint objects close to the LSST limiting magnitude, LSST (LSST Science Collaboration et al. 2009, 2017; giving additional chances for the small MBAs to be observed in Schwamb et al. 2018b; Ivezić et al. 2019; Bianco et al. conditions where they might have sufficient S/N to contribute 2022). The NES region, shown in Figure 3, is composed of to shape modeling. The opposite effect is observed for the ∼604 pointings covering in total ∼5800 deg spanning from 0° small PHAs and NEOs, which benefit in simulations without decl. to +10° ecliptic latitude. In order to make this goal the NES minisurvey (about a 6%–10% increase in the light- achievable with the non-WFD time, the NES minisurvey has curve inversion metrics). These objects are typically detected typically been implemented in the cadence simulations to close to Earth and so quickly become too faint to be detected. receive a smaller number of visits per field (∼250; as shown in Thus, pushing the time used for the NES minisurvey into Figure 2) compared to the WFD. The NES minisurvey includes additional WFD visits enables more observations where these observations taken in a combination of the griz filters only, small PHAs and NEOs are detectable and can have light curves where solar system objects are typically the brightest. The measured. The opposite effect can be seen for the larger PHAs observing time dedicated to the NES is explored in and NEOs. The larger PHAs and NEOs suffer a ∼10% drop in Section 4.1.3; here we focus on the impact of including or the light-curve metrics when the NES fields are excluded. excluding the NES minisurvey from the LSST. The NES minisurvey is crucial for inventorying the outer Because large PHAs/NEOs are more likely to be above the solar system. About half of the ecliptic plane is covered within limiting magnitude in an LSST image, surveying the NES the WFD footprint. Over the 10 yr span of the LSST, inner creates new opportunities to monitor the brightness of large 13 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 4. Possible tuning options for the LSST footprint from the v1.5 experiments. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. We have truncated the bottom panel’s y-axis for visibility. The change in the H = 15 Jupiter Trojan detections in some of the runs extends well above 1.2. PHAs/NEOs as they pass by Earth. The Jupiter Trojans also partially sampled without the NES observations, as discussed in Schwamb et al. (2018b). Two such cases are the Neptune take a significant hit when the NES is not included. This is likely due to their constrained positions on the sky. Trojans and the resonant TNO populations. Over the 10 yr Not captured in the MAF metrics are the benefits that the period, the vast majority of the leading Neptune Trojan L4 NES minisurvey provides to small body populations that are cloud is accessible only via observations of the NES as shown distributed asymmetrically across the sky. They would only be in Figure 5. Lin et al. (2019) find evidence for potential 14 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 5. The sky positions of the Neptune Trojan population from the Lin et al. (2021) model on 2025 June 22 (gold) and 2033 June 22 (gray). The two epochs represent early and late times in the LSST, respectively. The leading L4 cloud is at north, left-hand side of the plot. The dashed black lines represent ±10° ecliptic latitude. The solid blue line plots the center of the Galactic plane. The dashed blue lines delineate ±10° Galactic latitude. Probing the low-inclination L4 Neptune Trojans requires the inclusion of the NES in the LSST footprint. differences in the color distributions of the L4 (leading) and L5 enough to be visible with LSST images. If Planet Nine is bright (trailing) Neptune Trojans. The WFD and NES minisurvey enough to be imaged in single LSST exposures, the length of combined are capable of sampling both clouds with sufficiently the survey in combination with the repeated coverage of the large numbers to test this further. Including the NES region in survey footprint effectively eliminates the possibility of the LSST footprint enables characterization of the libration missing Planet Nine in high Galactic latitude fields owing to islands for the various mean motion resonances (MMRs) with coincidental overlap with another source. Closer to the Galactic Neptune (Schwamb et al. 2018b). Only observing half the plane, stellar crowding will be significant and identifying ecliptic with just the WFD and Galactic plane minisurvey sources will be difficult. This may require community- would impact the study of the resonant TNO populations, optimized search algorithms to look for Planet Nine in these which preferentially come to perihelion at certain locations on observations. Rubin Observatory is also exploring additional the sky (e.g., Gladman et al. 2012; Gladman & Volk 2021). options to enhance source extraction near the Galactic plane The NES minisurvey is crucially important for searching for (see Bosch et al. 2019). If current searches fail to find Planet additional distant planets in the solar system and testing the Nine, Rubin Observatory will put the best observational apparent orbital clustering of Sedna-like inner Oort Cloud constraints on the existence of Planet Nine over the next objects (IOCs; q > 50 au and a > 250 au) and extreme TNOs decade and will be the facility with the best chance of directly (ETNOs; objects on orbits with q > 42 au and a > 150 au).It imaging it (Trilling et al. 2018b). As noted in Section 2.3.4, has been proposed that a giant planet (“Planet Nine”) is Rubin Observatory’s SSP pipelines are only sensitive to gravitationally shepherding the distant planetesimals onto moving objects at heliocentric distances 100–150 au. We similar orbits with aligned orbital poles and longitudes of fully expect that there will be several community-led efforts to perihelion (Trujillo & Sheppard 2014; Batygin & Brown 2016; find very slow moving distant objects in the LSST transient Sheppard & Trujillo 2016; Batygin et al. 2019; Brown & catalogs to search for Planet Nine and explore the IOCs and Batygin 2019, 2021; Oldroyd & Trujillo 2021). Recent ETNOs. Therefore, it is still important to consider this science modeling by Brown & Batygin (2021) combined with case for LSST footprint considerations. constraints from the Zwicky Transient Facility (ZTF; Brown Even if Planet Nine is not visible in the LSST images, the & Batygin 2022) and the Dark Energy Survey (DES; Belyakov LSST would potentially be able to reveal its presence if the et al. 2022; Bernardinelli et al. 2022) predict Planet Nine to be orbital alignment holds with the increased LSST sample of residing at a semimajor axis of 700 au or higher. Although the ETNOs and IOCs and matches the Planet Nine predictions. current predictions made available in Brown (2022) do have Whether or not the Planet Nine theory is correct, the distant Planet Nine distributed over a wide range of ecliptic longitudes, IOCs and ETNOs are an important probe for studying the the most likely location of Planet Nine is close to the region origin and evolution of the very distant outer solar system and where the Galactic plane intersects the northern ecliptic (see testing alternatives to the Planet Nine theory (Morbidelli & Figure 6). The bulk of the predicted Planet Nine sky locations Levison 2004; Brasser et al. 2006, 2012; Gladman & are within the LSST footprint as implemented in the base- Chan 2006; Kaib et al. 2011; Zderic & Madigan 2020; line_v2.1_10yrs simulation, which includes the NES Emel’yanenko 2022; Huang et al. 2022). Observing across the minisurvey. Figure 7 presents the estimated on-sky V-band ecliptic will be crucial for creating a large enough sample to apparent magnitude distribution for Planet Nine from Brown alleviate the challenging observational biases currently dealt (2022) and the predicted LSST r-band limiting magnitudes from the baseline_v2.1_10yrs run. Over a wide range of with when combining the multiple data sets previously used possible V − r colors, Planet Nine could potentially be bright to identify and test the apparent orbital clustering 15 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. met: at least 18,000 deg with a median of 825 visits per field (Ivezić & the LSST Science Collaboration 2013). For extragalactic science, including cosmology and galaxy studies, and galactic science, such as the study of the Milky Way’s structure, there have been proposals from the community requesting more of the WFD to be shifted to low-extinction and less crowded sky (Lochner et al. 2018, 2022; Olsen et al. 2018). Other arguments have also been raised for shifting the WFD footprint further northward, including overlap with future DESI (Dark Energy Spectroscopic Instrument; Abareshi et al. 2022) and Nancy Grace Roman Space Telescope observations (Olsen et al. 2018; Capak et al. 2019b). As this is a zero-sum game, visits are taken from near the Galactic plane with high stellar crowding and dust extinction and redistributed north- ward above 0° decl. This results in a fraction of the WFD survey now covering the NES region as seen in the Baseline v2.0 and v2.1 footprints shown in Figure 2. The SCOC has made the recommendation to use this new footprint as shown in Figure 2 extending the WFD northward, although the final decl. limits of the WFD and the exact boundary of the Galactic/high dust extinction region can still be fine-tuned (Ivezić & the SCOC 2021). As implemented in baseline_v2.0_10yrs simulation, the revised WFD footprint has two decl. boundaries spanning from −72° to +12° decl. with an interstellar dust extinction cutoff at approximately E(B − V ) = 0.2 mag or A (V ) = 0.6 mag (Ivezić & the SCOC 2021; Yoachim 2022), where E(B − V ) is the dust reddening in magnitudes and A(V ) is the total V-band extinction. The northern boundary of the WFD varies with R.A. in this revised northward footprint; this is partly due to other additional constraints with the scheduler. We note that baseline_v2.1_10yrs simulation uses the same footprint as v2.0 but incorporates the Virgo Cluster (α = 12 hr, δ =+12°) into the WFD (Yoachim 2022). Figure 6. The simulated probability of Planet Nine (top) compared to the Expanding the WFD footprint northward will cover part of possible number of visits in the LSST footprint from the baseline_- v2.1_10yrs simulation (bottom). The Planet Nine probability density is the NES for “free” with the time charged to the WFD time taken from Brown (2022), which is based on 100,000 synthetic orbits and allocation, but part of the redistributed pointings in the 2°−12° physical properties of Planet Nine (including on-sky locations and V-band decl. band are at high ecliptic latitude because part of the apparent magnitudes) drawn from the distributions developed in Brown & ecliptic plane crosses the Galactic plane in the southern Batygin (2021), where we have removed the ones that are flagged as being ruled out by constraints from the ZTF (Brown & Batygin 2022) and the DES hemisphere. Transferring WFD visits from the Galactic bulge (Belyakov et al. 2022; Bernardinelli et al. 2022). The top panel has the LSST region will reduce the number of photometric data points footprint shaded by the number of observations that reach 5σ limiting available for generating rotational light curves for some MBAs magnitude of 24 in any filter. The most probable locations of Planet Nine are within the bulge, but how significant the impact is will depend within the NES region, but the full LSST footprint is required to search and probe the majority of the Brown & Batygin (2021) predicted Planet Nine on the exact shape of the footprint. The OCCs, NEOs, and parameter space. The plots are centered on α = 0 and δ = 0. R.A. and decl. PHAs are distributed across a wide range of ecliptic latitudes, lines are marked every 30°. so observations at higher ecliptic latitudes will still find small bodies in these populations. The same arguments that hold for (Brown & Batygin 2016, 2019; Shankman et al. 2017; outer solar system objects in Section 4.1.1 also apply in this Bernardinelli et al. 2020; Napier et al. 2021). case. Assuming a 2025 February 14 start date, Neptune’s on- sky position will have changed by about 1 hr in R.A. and 8° in 4.1.2. Extending the Wide–Fast–Deep Footprint Northward decl. by the end of LSST observations. Objects beyond 30 au will be moving slower than Neptune. Most of the TNOs and The NES minisurvey (as described in Section 4.1.1) was IOCs located in the NES at the start of the survey will remain in proposed when the northern limit of the WFD footprint was the NES throughout the duration of the LSST. As these distant initially set to be +2° decl. (see the baseline_retrofoot objects do not move very far on-sky during the 10 yr survey, simulation in Figure 2). The originally planned WFD sky any observations of the NES are beneficial for discovery as coverage used a simple cut in Galactic coordinates to identify long as not too much time is taken away from near ecliptic the boundary of the WFD with the Galactic plane/bulge pointings in the southern hemisphere. observing region (LSST Science Collaboration et al. 2017; In Figure 8, we evaluate the impact of the new northward Ivezić et al. 2019; Jones et al. 2020). Combining this boundary with the sky coverage requirements and visit constraints for the WFD sky coverage, comparing baseline_v2.0_10yrs WFD set the original northern decl. limit. What sky is included and baseline_retrofoot_v2.0_10yrs simulations. All within the WFD is a changeable LSST survey parameter, as simulations predating the v2.0 simulations start from a long as the SRD requirements for the WFD survey area are variation of the WFD with the old +2° decl. limit. The v2.0 16 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 7. The sky−magnitude parameter space for a simulated Planet Nine (bottom) compared to the possible LSST sky coverage and limiting magnitudes from the baseline_v2.1_10yrs simulation (top). The bottom left panel shows the median expected V-band apparent magnitude, and the bottom right panel has the maximum expected V-band magnitude from the distribution of Planet Nines, as described in Figure 6. Also shown are the LSST individual exposure median (top left) and 10 yr coadded (top right) r magnitude depths per pointing in the survey footprint. Since the optical color of the potential Planet Nine is not constrained, we do not apply any V − r color to allow for multiple comparisons depending on the reflectance model preferred by the reader. Observing the NES with Rubin Observatory is crucial for testing and constraining the Planet Nine parameter space. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. baseline also has additional changes to the observing cadence, 2.0/2.1 WFD footprint simulations that allow for a larger including tweaks to the rolling cadence implemented and the number of visits to be distributed across the sky. The v1.5 exposure time for u-band visits. For an apples-to-apples footprint family uses these additional visits to explore the comparison, the v2.0 release includes baseline_retro- impact of typically adding more northern visits in various foot_v2.0_10yrs, which uses the original v1.5–1.7 base- configurations, modifying the number of visits in the Galactic line WFD+NES footprint, leaving the other cadence plane and NES (sometimes in differing filters), and some parameters the same as the v2.0 simulation. The northward changes to the extent of the WFD footprint. The sky map WFD footprint produces a slight increase in discovery metrics showing the total numbers of visits per pointing in these v1.5 (all less than 5% change) for all populations except for the large simulations is shown in Figure 10. In general, adding visits Jupiter Trojans. There are also slight improvements in the light- northward enhances TNO discovery statistics, and in most curve metrics, with the smallest MBAs seeing more than a 10% cases, there are only small impacts on the ability to obtain light increase with the extended WFD footprint. These increases curves and produce shape inversion models of inner solar may be more significant than represented in the MAF metrics if system objects. Instead of adding a small number of visits, the stellar crowding was taken into account. Although this v1.7 WFD footprint experiments explore WFD variations comparison is to the v2.0 baseline, it will still hold true for on a dust-extinction-limited footprint with variable north/south the v2.1 baseline (baseline_v2.1_10yrs) that goes decl. limits. The total numbers of visits in these v1.7 WFD slightly more northward. We note that the addition of the footprint experiments are shown in Figure 11. Overall, Virgo Cluster is a minuscule change in area, and there are TNOs and outer solar system discoveries benefit the most, with negligible impacts to any of the solar system metrics compared the inner solar system object discoveries taking only a few to baseline_v2.0_10yrs (as discussed in Section 4.7.2). percent loss in discoveries. The light-curve metrics for the most For completeness, we briefly discuss the footprint experi- part see 5%–10% boosts in the various configurations of the ments performed in the v1.5 and v1.7 releases that led to the more dust-free WFD, but they start to decrease more revised northward WFD incorporated into the v2.0 and onward significantly for the smaller-sized MBAs, NEOs, and PHAs, LSST cadence simulations. The discovery and light-curve as less of the ecliptic that intersects with the Galactic plane in metrics are shown in Figures 4 and 9. The v1.5 footprint the southern hemisphere is included in the WFD and the simulations are set up with different overheads than the v1.7/ number of visits to those regions drops. Some caution needs to 17 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 8. Impact of the revised v2.0 LSST footprint with the northward and dust-extinction-limited WFD. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. be taken in interpreting this result, as this loss may be less than of detections in the simulations, this is likely contributing to the what is shown by the metrics as detection efficiency of solar variation observed in the light-curve metrics. Overall, these system objects (and by extension the ability to measure their v1.5 and 1.7 footprint experiments show that moving visits light curves) decreases in crowded fields. The Jupiter Trojans northward is an improvement and paved the way for the are constrained in set locations on the sky; with small numbers optimized v2.0/v2.1 WFD footprint and full LSST footprint. 18 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 9. Possible tuning options for the WFD survey footprint from the v1.7 experiments. As visits are taken away from the Galactic plane and bulge region, they are redistributed northward and southward to less dust extinction and less stellar crowded regions. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. 4.1.3. Varying the Fraction of Time Spent in the Non-WFD Regions discussed below show how covering these specific areas with visits ranging from 1% to 100% of the WFD cadence affect Three sets of simulations were done in which the visits to the metrics for solar system populations. NES and the Galactic plane were varied relative to the WFD The vary_NES family of simulations included coverage of the coverage. In these simulations, varying numbers of extra visits fields in the NES at 1% of the WFD level, at 5%–55% of the to the NES or the Galactic plane are added at the expense of removing that observing time from the WFD. The simulations WFD level in 5% increments, and at 75% and 100% of the WFD 19 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 10. The total number of visits in all filters after 10 yr for the v1.5 footprint experiment simulations. The DDFs are also visible as a collection of single fields receiving a higher number of observations than a WFD pointing. Each DDF receives approximately 1% of the total LSST observing time. The filterdist_indx2_v1.5_10yrs run does not include DDFs. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. level; the baseline simulation has the NES fields at 30% of the coverage levels for the NES (<10%), the discovery metrics for the WFD. The top panel of Figure 12 shows the discovery metrics for TNO populations are reduced by more than 5% relative to various solar system populations for these simulations. At low baseline, and most populations show increasing discoveries as the 20 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 11. The total number of visits in all filters after 10 yr for the v1.7 WFD survey footprint exploration simulations. As visits are taken away from the Galactic plane and bulge region, they are redistributed northward to pointings with less stellar crowding and dust extinction. The DDFs are also visible as a collection of single fields receiving a higher number of observations than a WFD pointing, with each DDF receiving approximately 1% of the total LSST observing time. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. NES coverage increases. Figure 13 shows the fraction of each (down to H = 6) that are observed in at least four filters is solar system population (relative to baseline) that is observed in at reduced by more than 20% compared to baseline; the NES must least fourofthe grizy filters as a function of the NES coverage. If cover at least 25% of the WFD to not reduce this metric by more the NES is covered at 15% of the WFD, the fraction of TNOs than 5%. The TNO populations are the most affected in both 21 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 12. Varying the time spent on the NES. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. Note: the light-curve inversion plot has been truncated for clarity. The MBAs and Jupiter Trojans extend beyond the plot for the 1.0 NES fraction. discovery and color light-curve metrics when the NES is not faint MBAs and faint Jupiter Trojans that are expected to have covered to at least 25% of the WFD level because they move light-curve measurements (bottom panel of Figure 12); all the slowly on-sky compared to closer-in solar system populations. populations generally improve in both the color light-curve and Most of them will not move enough over the 10 yr LSST time light-curve metrics as NES coverage increases. span to move from NES fields to WFD fields. Covering the NES The vary_GP family of simulations included coverage of at <25% of the WFD also significantly decreases the number of the fields in the Galactic plane at 1% of the WFD level, at 5%– 22 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 13. Color light-curve metrics with varying time spent on the NES from the v2.0 simulations. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. 55% of the WFD level in 5% increments, and at 75% and 100% curve metrics (see Figure 16) are also generally similar across of the WFD level. Figure 14 shows the resulting solar system the family of simulations, with losses in the fraction of faint metrics for discovery and light-curve inversion. The discovery populations observed in four of the grizy filters that hover metrics for different solar system populations are all within 5% around 5% for the simulations with higher threshold values of baseline for these simulations, and the fraction of each (above ∼0.4), with or without pencil beams; the simulations population that has observations in multiple filters is also with the lowest thresholds show an enhancement in the color relatively unaffected. However, when the Galactic plane is light-curve metric. However, for this entire family of simula- covered at >30% of the WFD level, the fractions of faint tions, the fractions of faint MBAs, Jupiter Trojans, NEOs, and MBAs, Jupiter Trojans, and PHAs with light-curve inversions PHAs with light-curve inversions all suffer >5% losses all drop by 5% or more (increasing losses with increasing compared to the baseline simulation (bottom panel of Galactic plane coverage) compared to the baseline simulations. Figure 15); again, this is likely due to additional time shifted This is likely simply a result of shifting time away from the away from the WFD fields. The set of simulations that cover WFD fields, decreasing the odds that the fainter solar system the priority map at >0.6–1.2 threshold with or without pencil objects are above detection thresholds multiple times in the beams generally keep the light-curve inversion losses for these reduced number of visits to their fields. populations to between 10% and 20% compared to baseline. The plane_priority family of simulations varies how The simulations with four larger or 20 smaller Galactic plane different regions of the Galactic plane are covered based on pencil beam fields added in addition to the plane priority maps a priority map of the Galactic plane from the Rubin have worse light-curve inversion metrics for all solar system Observatory LSST Stars, Milky Way, and Local Volume and populations than simulations with just the priority maps. Transients and Variable Stars science collaborations. Some of these simulations also have pencil beam fields in areas of the Galactic plane that the WFD is not planned to cover. These targeted pencil beam fields would be visited at the same level 4.2. Exposure Times and Snaps as the WFD. The Galactic plane plane_priority simula- In this section we explore the various options for the total tions were completed with and without pencil beam fields, and exposure time per visit and the number of observations two additional simulations were done with just four larger or 20 (“snaps”) taken at each visit. Both parameters directly impact smaller Galactic plane pencil beam fields (the pencil_fs the amount of open shutter time available and therefore how simulations). The discovery metrics for different solar system many exposures can be taken on any given night and in total by populations are almost all within 5% of baseline for these the survey per filter. The visit exposure time also impacts the simulations, with only faint MBAs and faint Jupiter Trojans individual image depth, increasing or decreasing the resulting dropping slightly below those thresholds for the priority image’s5σ limiting magnitude. threshold at 0.1–0.2 (top panel of Figure 15). The color light- 23 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 14. Varying the time spent on the Galactic plane (v2.0 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. 4.2.1. Snaps Section 4.2.2). The original plan was for the Rubin The LSST cadence is currently planned with two Observatory data management pipelines to compare the two snaps in order to identify and flag pixel-level artifacts exposures of equal length dubbed “snaps,” nominally 15 s each, to be taken back-to-back at each visit to an on-sky (e.g., cosmic rays). Source detection would be performed on pointing, except in the case of u-band observing (see the image resulting from coadding the two exposures 24 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 15. Varying the time spent on the Galactic plane (v2.1 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. (Ivezić & the LSST Science Collaboration 2013;Ivezić et al. There now exist many algorithms published in the literature for 2019). We note that the Rubin SSP pipelines’ discovery identifying cosmic rays in single astronomical images (e.g., algorithm is agnostic to the number of snaps per visit, as it Rhoads 2000; van Dokkum 2001;Shamir 2005; McCully et al. uses the transient sources detected in the coadded snaps 2018). If these algorithms work well on LSSTCam images, there image as input (Myers et al. 2013;Jurić et al. 2020). may be no strong reason for taking two snaps at each visit. The 25 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 16. Color light-curve metrics with varying time spent on the Galactic plane (v2.1 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Table 4 Varying Snaps Exposures LSST Cadence Total No. Area with per Visit Simulation Name of On-sky >825 Visits Visits (deg ) 2 × 15 s (current SRD requirement) baseline_nexp2_v1.7_10yrs 2,045,493 17,982.71 1 × 30 s baseline_nexp1_v1.7_10yrs 2,208,619 18,190.85 decision on whether to implement one snap or two snaps per visit enabling other science. In the v1.7 simulations, the extra visits will be made during commissioning of the LSSTCam and the gained in the one snap case were divided out evenly between the Rubin data management pipelines (Ivezić et al. 2019) when the WFD and other parts of the simulation’s survey footprint. This feasibility of single exposure cosmic ray rejection can be tested produces an increase in both the detection and the light-curve and the impact from satellite constellation streaks can be properly metrics (see Figure 17). The detection metrics for the small size assessed (see Section 5.4). With two snaps, each planned visit has end increase by a few percent. The extra visits provide additional two camera readouts and two camera shutter openings and chances for those objects near the survey brightness limit to get closings. Although the readout time and the movement of the above the image 5σ limiting magnitude and be detected. The camera shutter are relatively quick (well less than a minute),the largest bodies see only a very slight increase because the majority summed time lost to these overheads over the entire 10 yr survey of times when they land within an exposure they are already is nonnegligible in the case of the two snaps observing cadence. brighter than the limiting magnitude. The largest enhancement is As can be seen in Table 4 for the v1.7 family of simulations (the seen with the light-curve inversion metrics, especially for the most recent LSST cadence simulations exploring the number of small end of the size distribution, where we find a >20% boost snaps), switching to one snap generates an ∼8% gain in on-sky across the MBAs, PHAs, NEOs, and Jupiter Trojans. Like the visits and an increase in the sky coverage reaching the WFD goal case for discovery, the extra observations provide more of 825 visits per pointing. opportunities for better temporal coverage to probe rotations and The impact of switching to a single-snap cadence will depend perform shape inversion. Since only six detections are required for on what the added exposures are used for. The SCOC has not yet discovery, it is the light-curve inversion metric that shows the true made any decision about snaps and how to partition out the extra benefits for color and light-curve measurements from the extra on- visits. If a significant portion of the gained visits can be distributed sky observing. across the entire LSST footprint or WFD and NES, the increase in As noted in Schwamb et al. (2018b), there is some extra exposures will add sky coverage and/or temporal coverage that information that is potentially gained with two snaps per visit. will help with detection and monitoring of small bodies while Although the SSP pipelines are not currently planned to use 26 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 17. Varying the number of snaps per visit (v1.7 simulations). The case for the reference simulation with two snaps is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light- curve inversion metrics. any information from the individual snaps, bespoke community and (2) brightness variations (on the order of seconds) to be software could be developed to take advantage of the two extracted from the streaks for ultrafast rotators. Only a very tiny exposures per visit. For those small body populations moving fraction of the asteroids discovered will be rotating fast enough fast enough to be significantly trailed in the LSST images, such that sub-30 s resolution will be useful (Pravec & Harris 2000; as NEOs and PHAs, the sequential snaps allow for (1) the on- Masiero et al. 2009; Warner et al. 2009, 2021; Hergenrother & sky direction of motion to be measured from the two streaks Whiteley 2011; Chang et al. 2014, 2019, 2021, 2022). These 27 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. are very limited benefits compared to the gains to all solar discovery metrics is greater for fainter PHA and NEOs with system populations from an extra 8% of on-sky observing time. 16 < H < 22 than for bodies with H < 16. The enhanced Therefore, we recommend incorporating single-snap visits into discovery caused by the greater coverage is due to the fact that the LSST cadence, if feasible. these closer-in objects tend to be detected at viewing geometries when they are closer to Earth and are thus brighter to compensate for their smaller size. Examples of faint, close-in asteroids whose 4.2.2. Long u-band Observations discoveries are favored by the shorter cadence when they The long_uX and u_long simulations explore the impact approach Earth include asteroids on Earth-similar orbits and of using a single longer u-band exposure versus two shorter (15 meteoroids (e.g., Kwiatkowski et al. 2009;Granvik et al. 2012; s) exposures in the baseline. This was investigated since, Bolin et al. 2014, 2020a; Jedicke et al. 2018;Shoberetal. 2019; because of the low level of sky background in u band, readout de la Fuente Marcos & de la Fuente Marcos 2020b, 2022; noise has a larger impact than in redder bands, and a single Fedorets et al. 2020b; Naidu et al. 2021). longer exposure allows us to significantly improve the u-band When exposure times are increased to more than 30 s, more depth (see, e.g., Jones et al. 2020). The u_long family varies distant objects have improved discoveries, but the discovery of the duration of the single u-band exposure (30, 40, 50, or 60 s). PHAs and NEOs diminishes. The decreased discovery of PHAs It is expected that longer u-band exposures (40 or 50 s) will be could be due to the decreased sky coverage in the longer-exposure advantageous for the detection of faint activity around solar scheme compared to the shorter-exposure scheme. The degrada- system objects. Since the u band also encompasses emission tion in the number of PHAs and NEOs in the longer exposures from the CN radical around 388 nm (and to a lesser extent from could also be due to trailing losses from their higher rate of motion the NH radical), there might be slight gains for active comets (e.g., Shao et al. 2014;Yeetal. 2019). One additional factor to inside 3 au, increasing with decreasing heliocentric distances, consider in the longer exposure times is that they will be more but this has not been modeled in detail yet. However, longer u- susceptible to images being compromised from satellite trails, band exposure times (starting marginally with 50 s but more which is more likely in longer exposures (Tyson et al. 2020);see strongly for 60 s) result in a lower number of observations Section 5.4 for a detailed discussion. being performed in other filters and thus decrease the number The effect of shortening the exposure time improves the light- of faint Jupiter Trojans and PHAs detected, as well as the curve inversion metrics for all dynamical groups of objects number of faint objects for which we can perform light-curve included in the v2.1 cadence simulations as seen in the bottom inversion, as illustrated in Figure 18. The long_uX uses a 50 s panel of Figure 20. Shorter exposure times generally improve the exposure, either keeping the same number of visits (long_u1) light-curve inversion metrics owing to the improved coverage and or reducing it (long_u2). Both of these tend to be worse than improved density of detections enabledbyshorter exposure the baseline for solar system objects in terms of light-curve cadences. The magnitude of improvement varies by dynamical inversion in particular for faint objects, as illustrated in class. For faint PHAs and NEOs with H = 19, the light-curve Figures 19 and 18. This results from the fact that light-curve metric is almost doubled, with 20 and 22 s exposures compared to inversion requires a certain number of observations above a the baseline cadence. Larger PHAs and NEOs with H = 16 see a certain S/N threshold, which might not be met for some objects moderate improvement as well with the shorter exposures. The in bluer filters, where most solar system bodies are fainter. The higher density of coverage will also be useful for the study of the long_u2 family performs better for both detection and light- rotation states of Jupiter Trojans and asteroid family members in curve inversion metrics and was identified as a good the main belt (Hanuš 2018), e.g., as shown by the increase in the compromise as long as it is not done together with any of the light-curve metrics for Trojans and MBAs. The benefits of wider bluer_indxXX options mentioned in Section 4.3, which is and more frequent coverage of the sky to light-curve inversion shifting more visits to blue filters over redder filters. may also extend to the monitoring and detection of activity within the asteroid belt (e.g., Moreno et al. 2017). The improvement for 4.2.3. Other Variations of Exposure Times more close-in objects may be explained by their higher sky-plane motion, placing this in a wider range of possible areas of sky The visits in the v… 2.0 survey simulations are typically set to positions that is more easily covered with a shorter cadence. A 2 × 15 s exposures in the grizy filters, while the u band has good compromise exposure time for obtaining favorable 1 × 30 s exposures. A series of simulations (v2.1 shave) has discoveries for inner and outer solar system objects, as well as been run to explore the impact of different exposure times on the dense light curves, seems to be the 30 s exposure cadence. survey metrics compared to the family’s baseline simulation. As An additional simulation, vary_expt_v2.0_10yrs, was seen in the top panel of Figure 20, the relative effect on the designed to test the results of varying the exposure times discovery rate of TNOs, faint OCCs, and faint MBAs diminishes between 20 and 100 s in the ugrizy filters to provide significantly with shorter exposure times compared to the baseline consistency in the image depth in different filters. As seen in exposure time configuration, as the 5σ limiting magnitude the top panel of Figure 19, varying the exposure time between decreases with exposure time. Shorter exposure times have a 20 and 100 s results in poorer discovery metrics relative to the greater effect on fainter absolute magnitude TNOs, dropping the baseline simulation for all classes of solar system objects used discovery metrics by more than ∼5% for TNOs with 6 < H < 8 in the simulations. This is due to the fact that the longer compared to TNOs with H < 6. The effect is similar for OCCs, exposures result in an overall decrease in survey coverage and a with the discovery metrics decreasing by ∼5% for OCCs with decreased chance to detect moving objects. As seen in the 8 < H < 12 compared to OCCs with H < 8. The discovery of NEOs and PHAs does see a small improvement with the shorter bottom panel of Figure 19, the effect on light-curve inversion exposure cadences owing to increased sky coverage resulting metric is also worse for all classes of solar system objects. from the shorter exposure times allowing for more exposures to be Therefore, varying the exposure times to achieve uniform visit taken (e.g., Jedicke et al. 2016). This improvement in the depth is not recommended. 28 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 18. Changing the length of the u-band exposures in the v1.7 simulations. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. example), or modifying how observations in different filters are 4.3. Filter Cadence and Filter Distribution interspersed within a night or throughout a lunation. First, we This section explores decisions focused around the choice of examine the effects of increasing the number of observations in u filter, i.e., changing the distribution of observations across filters and g bands compared to the baseline. Next, we explore the consequence of imposing that a certain number of observations (to increase the total number of observations in bluer filters, for 29 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 19. The impact of skewing the filter distribution bluer, increasing the exposure time of the u-band observations, and the effect of varying exposure time per visit (vary_expt_v2.0_10yrs). All the simulations presented in this figure are from the v2.0 runs. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. are performed in various combinations of filters each year. Lastly, The bluer_indxXX family of simulations have a bluer filter we investigate changing the cadence of observations in g band, distribution, increasing the number of exposures in g,or u and g taking advantage of bright time to schedule extra visits and reduce filters compared to the baseline (the filter balance in the baseline is the gap between successive observations of a given field in “u”: 0.07; “g”:0.09; “r”:0.22; “i”:0.22; “z”:0.20; “y”:0.20).This g band. is done by removing visits in redder filters to redistribute them 30 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 20. Variations on the effective exposure time per visit in the v2.1 cadence simulations. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Except for the baseline simulation, which has 2 × 15 s visits in grizy and 1 × 30 s in u, all other simulations in this run had single exposure visits per pointing. Top: discovery metrics. Bottom: light-curve inversion metrics. between u and g. Similarly to the families discussed above, from CN and C radicals, respectively. As discussed above and increasing the number of exposures in u or g band results in a illustrated in Figure 21,a u-heavy distribution induces a severe decrease of the number of faint objects for which light- significant decrease in the detection of faint solar system objects curve inversion is possible. Even though this has not been that are fainter in u band, as illustrated in Table 1. modeled yet, active objects close to the Sun might benefitfrom Figure 22 presents a set of simulations where emphasis is put increased u and g coverage, as these filter encompass emissions on obtaining a handful of exposures with a seeing <0 8 each 31 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. year, varying the weight put on that constraint and the at which SSP can detect moving sources. The time between combinations of filters for which it has to be met (whether i repeat visits also directly impacts the number of pairs observed and y are included or not). These simulations come as a request per night and thus the total area searchable for solar system for extragalactic science cases. Ensuring that there are yearly objects. We aim to find the best pair time separation that good seeing images in several filters enhances strong lensing increases the distances that SSP is sensitive to without making detection (Verma et al. 2019) and galaxy studies (Ferguson a significant trade-off in observing efficiency. This would allow et al. 2021). In general, requirements of having a minimum the Rubin SSP to detect more distant TNOs while not number of good seeing images per year in various bandpasses compromising the discovery and characterization of the more do not impact strongly our discovery or light-curve metrics inward solar system populations. We note that the tunable (except for the good_seeing_gsw1.0_v2.1_10yrs and parameter here is the Rubin scheduler’s goal for spacing the good_seeing_u_gsw0.0_v2.1_10yrs). repeat visits in a given night. In reality, this will be a The cadence_drive family of simulations investigates distribution centered about the ideal value the Rubin scheduler reducing long gaps between g-band visits over a month by is aiming for. This is shown in Figure 24 for three examples requiring a certain number of fill-in visits each night during from the v1.7 pair_times simulations that take mixed filters bright time. Adding g-band visits during full moon time (and with ideal separations between 11, 22, 33, 44, and 55 minutes. consequently reducing the number of visits in redder bands) is Solar system objects appear to move fastest on-sky when generally detrimental for solar system objects, and in particular they are at opposition, where the apparent motion is dominated for light-curve inversion of faint Jupiter Trojans as illustrated in by the parallax induced by Earth’s movement. The on-sky rate Figure 23. A small number (30) of contiguous visits might be of motion at opposition for a body exterior to Earth’s orbit on a acceptable, but in general the lowest possible number of g-band circular and coplanar orbit can be defined as fill-in visits is preferable. -0.5 1 - r dq ⎛ h ⎞ = 148 ,3() ⎜⎟ 4.4. Visits within a Night dt r - 1 ⎝ ⎠ The Rubin SSP pipelines will search nightly image pairs for where r is the body’s heliocentric distance in astronomical new moving sources. Once the orbit of a solar system object is dq units and is the apparent motion at opposition in arcseconds known sufficiently well, SSP will be able to predict the orbit dt and identify previously known small bodies in single LSST per hour (Luu & Jewitt 1988). We assume 140 mas as a observations, but throughout the entire 10 yr new solar system conservative estimate for the astrometric uncertainty for discoveries will be made (Myers et al. 2013; Jurić et al. 2020). sources near the LSST detection limit and a 3σ positional shift The majority of the TNOs and MBAs will be picked up within for the SSP pipelines to successfully identify the moving object the first 2 yr of the survey, but new comets, NEOs, and ISOs as a new source in the second observation (M. Jurić 2022, will continue to be discovered across the duration of the LSST private communication). This translates to solar system bodies (Eggl et al. 2019). Thus, it is important that nightly pairs be having to move at least 0 5 between the visits in order to taken over the full span of the LSST. become detectable by SSP, setting a minimum speed limit. In The LSST SRD (Ivezić & the LSST Science Collabora- Figure 25, we estimate SSP’s motion limit for the range of pair tion 2013) requires at least two observations per night at each separations, including those explored in the pair_times observed pointing in order to facilitate accurate removal of the simulations. The solid line represents the opposition on-sky solar system “cruft” that will pollute the millions of transient astrophysical LSST alerts sent out. A transient only seen in one rate of motion as calculated from Equation (3). but not in a repeat observation on the same night will most The bulk of the classical Kuiper Belt extends from ∼42 to likely be due to a previously undiscovered moving small body. 47.7 au, the 2:1 MMR with Neptune, but the Kuiper Belt’s Multiple observations in the same night also help differentiate scattered/scattering disk and detached/high-perihelion TNO inner solar system objects from outer solar system bodies. population (with perihelion at ∼50–80 au) do extend well Additionally, these repeat visits provide temporal and color beyond that (Trujillo & Brown 2001; Petit et al. 2011; Adams information that can be used to probe the evolution of et al. 2014; Bannister et al. 2018; Bernardinelli et al. 2022). astrophysical transients (e.g., Bianco et al. 2019; Setzer Separations longer than 18 minutes are needed to search for et al. 2019; Andreoni et al. 2022; Li et al. 2022; Lochner objects beyond 80 au. Separations longer than 33 minutes start et al. 2022) and minor planets. Below we explore several to slightly negatively affect the discovery metrics and proposed options on the number, time separation, and filter significantly enhance the light-curve metrics, as plotted in choices of these intranight visits. Figure 26. The loss of discovery at fainter absolute magnitudes is less than 5% even at 55-minute spacings. As the time gap gets longer, the pairs are more vulnerable to interruptions, 4.4.1. Separation between Nightly Pairs mostly from weather. The fraction of gri pairs peaks at 22 Nightly pairs in combinations of the g, r, and i filters are the minutes, but the total visits and the on-sky area reaching 850 most conducive to finding solar system objects. We explore the visits both increase with longer pair separations. The light- intranight separations in these combinations of filters. As curve inversion metrics go up with longer gaps between discussed in Section 2.3.4, the SSP pipelines require that intranight visits, due to the increase in the total number of motion be seen between the two exposures (Myers et al. 2013; visits, with a larger number of singleton images that are spread Jurić et al. 2020). If a solar system body has not moved out across the observable sky (see Table 5). Having the Rubin sufficiently for it to be identified as a new transient source in scheduler aim for the two visits to be separated by 33 minutes the next visit, SSP will not be able to spot that moving object. is the best compromise between optimizing the number of The separation between nightly pairs sets the farthest distance nightly pairs completed and heliocentric distance probed. We 32 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 21. Additional options for tuning the filter distribution (v1.5 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. note that the SCOC moved the LSST baseline strategy from various combinations. These simulations begin the extended aiming for 22-minute nightly pair separations to 33-minute pair separation either in Year 1 (delayed-1) or after Year 5 ones from the v2.0 simulations onward (Ivezić & the (delayed1827). This simulation family explores the impact SCOC 2021). of executing the long gaps strategy every night and less The v2.0 long_gaps_np (long gaps, no pairs) simulations frequently, where the nightsoff parameter (the number of extend the time between nightly repeat exposures to 2–7hrin sequential nights with no long gap sequences) is varied. On the 33 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 22. The impact of adding a requirement for three “good seeing” (seeing < 0 8) images per year in various bandpasses (v2.0 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The baseline_v2.1_10yrs includes the good seeing requirement for r and i bands as the default. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. nights when the long gap observing is not active, the scheduler for addressing the temporal coverage of fast transients are aims for 33-minute nightly pair simulations like the v2.0 explored in Section 4.4.4. As seen in Table 5, the fraction of gri baseline survey. These simulations are one option explored to pairs is largest when the Rubin scheduler is tasked with 33- potentially better capture fast-evolving astrophysical phenom- minute pair spacings. Therefore, these hours-long separations ena, as suggested by Bellm et al. (2022); additional strategies are not going to be efficient in generating nightly pairs 34 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 23. Investigating ways of reducing the gaps between g-band visits over a month (v1.7 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. conducive for the moving object search. This can be seen in the in intranight visits occurring every night have less impact. The discovery and light-curve metrics displayed in Figure 27. less time devoted to the large time gap pair observing, the less Across all populations, the light-curve metrics and detections severe the hit to the discovery and light-curve metrics. decrease. The increased sensitivity to objects beyond 150 au is Nonetheless, 33-minute pair separations are better optimized not worth the trade-off purely from a planetary astronomy for outer solar system discoveries and the completion of repeat perspective, but the simulations that do not have the long gaps visits within the night. 35 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 24. Distribution of nightly pair separations across the WFD and the NES from three simulations that make up the v1.7 pair_times family. The histograms are truncated at 120 minutes. for, and having same-filter nightly pairs reduces the number of different nights (and thus different longitudes) an object might be observed in that filter; for faint Jupiter Trojans, this appears to reduce the odds that successful detections in a given filter span the required range of longitudes. 4.4.3. Suppressing Extra Visits In the baseline cadence (baseline_v2.1_10yrs),upto 20% of the pointings are visited more than twice per night. By adding an additional basis function to suppress these repeat visits to the Rubin scheduler algorithm, the additional visits can be distributed to different nights, thus changing the internight cadence or season length for a given field. The suppress repeats (no_repeat_rpw) family of simulations (as shown in Figure 30) explores these changes by considering six different values for the weight of the suppression factor, indicated as rpw, namely 1, 2, 5, 10, 20, and 100. This number basically Figure 25. The opposition on-sky motion observed on Earth as a function of different heliocentric distance (solid line). The colored points represent the reflects how strongly the suppress-revisits basis function calculated slowest motion/distance detectable by the Rubin SSP pipeline for a influences the scheduler: the higher the number, the lower the range of nightly pair spacings. number of revisits per night will be. Note that some regions of the sky will still be observed more than two times within a 4.4.2. Filter Choices for Repeat Visits in a Night night if they are included in overlapping pointings. An immediate consequence of redistributing the visits over In the v1.5 simulations, cases were run with nightly pairs of different nights is a decreased total area with more than 825 visits performed in either matching filters (baseline_same- visits per pointings, from a negligible effect (0.1% at filt_v1.5_10yrs) or in mixed filters (baseline_- rpw = 1) to a more significant effect of 5% at rpw = 20. v1.5_10yrs). The discovery metrics for solar system However, because of the extended timeline, the discovery populations are largely unaffected by the choice (top panel of metrics are generally improved with respect to the WFD: a Figure 28). This is because the mixed-filter pairs in the cadence suppressing factor between 2 and 10 will increase the discovery simulations contain filter pairs such as g− r and r− i,where the rate for all the different families, while for rpw = 1, 20, or 100, colors of solar system objects allow detections in both filters (we there is only a marginal decrease (0.005%) in the discovery note that r− i pairings are better than g− r pairings for the rate of faint TNOs and bright comets from the Oort Cloud. A reddest objects like TNOs). Similarly to what is shown in suppressing factor equal to or larger than 10 will also impact Figure 29, the color light-curve metrics for different populations the metrics of light-curve inversion, reducing up to ≈10% the are not significantly affected by the choice of same or mixed number of faint MBAs and Jupiter Trojans for which inversion filters; there is likely an advantage to having mixed filters within will be feasible. Summarizing, the “suppress visit” family the same night in that it could provide a single-night color cadence produces negligible effects on solar system science, estimate for objects that rotate slowly compared to the visit with a marginal improvement on the discovery rates for separation. For faint NEOs and MBAs, nightly pairs in the same rpw = 2 and 5 and a marginal decrement of the number of filter do boost the light-curve inversion metric by 15%–20% faint objects for which we will be able to perform light-curve (bottom panel of Figure 28). This is likely due to nightly pairs of inversion for rpw = 10, 20, or 100. faint objects that are only detectable in a small number of filters. However, faint Jupiter Trojans suffer a 30% loss in the light-curve 4.4.4. Third Visits in a Night metric for the same-filter pairs. This is likely related to the light- curve metric requirement that observations in a filter span at least There is a strong desire among other Rubin Observatory 90° in ecliptic longitude. The Jupiter Trojans move more slowly LSST Science Collaborations to add a third visit in a different on-sky compared to the other populations this metric is calculated filter to aid in capturing and identifying fast (<1 day) transients 36 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 26. Changing the ideal nightly pair separation (v1.7 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. by adding more color information (see Bianco et al. 2019) to presto_half simulations explore the effect of adding the the base survey of nightly pairs of visits that are separated by third image/triplet every other night rather than every night of ∼20–30 minutes. The presto_gap family of simulations the cadence, while the presto_gap_mix has a wider explores the effects of adding a third visit to the night’s visits separation and difference in colors between the initial pair after a time period of 1.5–4 hr. Within the presto family of and the third visit (e.g., g + i, r + z, i + y rather than g + r, r + simulations, there are two significant subfamilies. The i, i + z initial pairs). 37 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 5 Diagnostics for the LSST Cadence Simulations Changing the Desired Separation between Nightly Pairs LSST Cadence Simulation Name Ideal Separation Total No. of Area with Mean Fraction (rms) between of On-sky > 825 Visits of WFD+NES Visits Nightly Pairs Visits (deg ) in 15-to-60-minute (minutes) Separated Pairs g, r,or i Filters Only pair_times_11_v1.7_10yrs 11 1,947,985 14,356.96 0.240 (0.061) baseline_nexp2_v1.7_10yrs/pair_times_22_v1.7_10yrs 22 2,045,493 17,982.71 0.586 (0.055) pair_times_33_v1.7_10yrs 33 2,075,493 18,076.71 0.546 (0.057) pair_times_44_v1.7_10yrs 44 2,089,977 18,104.40 0.475 (0.061) pair_times_55_v1.7_10yrs 55 2,100,189 18,108.60 0.398 (0.061) Notes. These simulations used 2 × 15 s snaps per visit. 15-minute separations cover the full classical Kuiper Belt; 60 minutes was chosen as the upper limit because the bulk of the nightly pairs in these runs are separated by less than this value (see Figure 24). The main impact of adding this third visit is to dramatically longer gaps for the potential third visit in the night, could decrease the amount of well-covered survey area (see constitute a path toward satisfying the desires of other science Figure 31). This would have a large negative impact on goals without unduly compromising solar system science. The science cases where the objects are sparse on the sky such as impact of the loss of sky area covered in all of these third visit discovering rare objects (e.g., ISOs) or the onset of activity on simulations on the detectability of rare but high-value targets that are sparse on the sky, such as ISOs or very distant extreme solar system objects. The other large effect of adding the third TNOs (ETNOs and IOCs), needs additional simulations with visit is seen in the solar system object detection and light-curve these populations added. The addition of the OCCs to the later inversion metrics and illustrated in Figure 32. Although there is versions of the simulations, which are much more numerous on some improvement in the detection of the brighter solar system the sky than either ISOs or Sedna-like objects, and the objects at the shorter gap lengths in the 1.5–2.0 hr regime (see, corresponding drop in OCC discovery when adding the third e.g., the presto_gap1.5_mix simulation in Figure 32), this visit show the downside of adding the third visit on discovering is not a high priority for the large-aperture capabilities of Rubin the rarer solar system populations. Observatory. For the vast majority of the other simulations and solar system populations, this family of simulations produces a 20%–75% decrease in the light-curve inversion metrics, well 4.5. Rolling Cadence beyond our threshold for flagging these simulation families as bad for solar system science. The impacts are less dramatic for Spreading the 825 observations of each field in the WFD the presto_half subfamily, as might be expected, since the evenly over the periods that they are observable, over 10 yr, third visit is only carried out 50% of the time. The impact of the corresponds to an observation of each field every three to four _mix version of a simulation (with the wider spread of nights, on average. As this is a relatively low cadence for some observed colors in the third visit) is always worse than the science topics (notably transients), a proposed pattern of corresponding “nonmixed” simulation run. observations increases the frequency in certain areas of the sky As an alternative to the presto families discussed above, in some years, at the cost of a lower cadence elsewhere, and the long_gaps_nightsoffN family (not to be confused then reverses the pattern the following year. This is referred to with the long_gaps_np family of simulations considered in as a rolling cadence. There are a variety of flavors of this Section 4.4.1) also adds a third visit in the same filter as one of approach, depending on how many stripes each half of the sky the pairs (like the presto family). However, unlike the (north/south) is divided into and the “strength” of the rolling, presto families, (1) the third visit forming the triplet is in one i.e., the fraction of the time spent in the “on” stripes compared of the same filters as the earlier pair, (2) it only occurs if the to the “off” ones (see Figure 33 for an illustration of these first pair is in the griz filters, and (3) it occurs after a longer 2–7 patterns). No rolling cadence entirely neglects the “off” stripes, hr gap from the initial pair than the standard ∼33-minute gap. but in some cases these areas see only a few observations in the This is done every N nights (N = 0K7), for example, entire year, to support template building. Over the 10 yr of the long_gaps_nightsoff7 has the long gaps every seven survey, the pattern of on/off stripes balances out to give nights, and long_gaps_nightsoff0 has “zero nights off” uniform coverage across the whole WFD area. For the majority and the long gap third visit/triplets are done every night. These of simulations rolling cadence is only applied to the WFD area families additionally come in two flavors, delayed-1 and (not the bulge, NES, or other “extended” survey areas) and is delayed1827, where the third visit/triplets start either not used in the first and last 1.5 yr of the survey. Video immediately before the start of the survey (night −1) or in animations of three example rolling cadence scenarios are survey year 5 (night 1827), respectively. available (via the online version of the paper) in Figures 34–36. Overall this family of simulations has much smaller The effect of rolling cadence is generally seen as positive for detrimental effects (<10%) on the area covered (final third of most science cases, for example, having a denser coverage of Figure 31) and most solar system metrics, except for where this light curves in the “on” stripes enhances transient science, and is done every or almost every night (the _nightsoff0 and rolling cadence is included in the baseline v2.0 simulations. _nightsoff1 simulations), which hit the area and light- However, there are positive and negative effects that vary with curve inversion metrics hard (20%–60%; see final third of the pattern and strength of the rolling cadence. There is little Figure 32). These families of survey strategy simulations, with difference between patterns that split the WFD into two or three 38 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 27. Impact of various scenarios for lengthening the gap between pairs to be variable in the range of 2–7hr (v2.0 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. The y-axis is truncated in the light-curve inversion plot. The Jupiter Trojans extend below the y-axis range for long_gaps_np_nightsoff0_delayed-1_v2.0_10yrs. The baseline simulation has an ideal separation of 33 minutes. stripes north and south of Cerro Pachón, but a more extreme Figure 37 is for faint Jupiter Trojans, which is not surprising, as six-stripe pattern, especially at high rolling strength, has more the Trojan clouds have a limited spatial extent that is in an significant effects on both discovery and light-curve metrics approximately fixed direction in a given season, relative to (Figure 37). Such a pattern is also vulnerable to extended where the corresponding planet is. As the cloud may fall into periods of bad weather in one season, resulting in uneven final either an on or off stripe in a given season, Jupiter Trojans coadded survey depth, so it is not favored for many areas of experience feast or famine in terms of observations, which may LSST science. The largest variability in the metrics shown in not even out over the years in the same way that more distant 39 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 28. Nightly pairs in the same vs. different filters (v1.5 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. populations (TNOs) will—this will depend on the precise potentially affect follow-up of rare objects like ISOs, or impact timing and choice of band patterns in the final survey. target choice for the European Space Agency’s (ESA) Comet A remaining concern with rolling cadence is the possibility that Interceptor (expected to launch in 2029; Snodgrass & Jones 2019), individual objects of interest may be missed, or more likely be in the unlucky case that a suitable long-period comet is missed for discovered later than they could have been if they first brighten a year. In general, discovery metrics for OCCs are not strongly above LSST detection limits in an off stripe. This could affected by rolling cadence, so this is not seen as a major risk for 40 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 29. Color light-curve metrics for observing strategies with nightly pairs in the same vs. different filters (v1.5 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. the mission. Further study of the effect of rolling cadence on how 4.6.1. Fraction of Time Devoted to DDFs early we might discover ISO and OCC targets is ongoing. The time balancing of DDF and WFD observations within the LSST has a noticeable impact on overall solar system detections and light-curve inversion capability. Simulations 4.6. Deep Drilling Field Observing with a larger portion of survey time for DDFs were previously The DDFs are a key component of LSST’s structure, currently trailed in the v1.6 sims (ddf_heavy_) but were rejected, as allocated ∼5% of the total survey time in the latest survey they produced significant negative impacts on all solar system simulation baselines. There are five confirmed DDF pointings populations and their metrics, as well as failing to meet some of (Table 6), which will be observed with a completely different the key science requirements for the WFD. cadence from the WFD: a higher sampling rate, as well as a The v2.0 simulations are the latest set of simulations that different sampling of filters (Jones et al. 2020). The locations of explore varying the fraction of total survey time allocated to the DDF pointings were largely motivated by both Galactic and DDFs. They test a more conservative variation of ±3% survey extragalactic science goals (Bell & Hermes 2018;Brandt etal. time spent on DDFs from the baseline value of 5%. In these 2018;Holwerdaetal. 2018; Scolnic et al. 2018; Capak et al. simulations, any extra observing time is evenly distributed 2019a). However, the ability to stack the denser sampling means across the remaining components of the LSST. Both options are satisfactory for the discovery and light-curve inversion of that these fields also provide a small, deeper data set than the solar system objects, for most or all populations (Figure 38). WFD (LSST Science Collaboration et al. 2009). The simulation with 8% of time allocated to the DDFs The DDFs provide a limited but strategic improvement to the (ddf_frac_ddf_per1.6 ) provides slightly worse results solar system science expected from LSST (Figure 38). The for discovery and light-curve inversion metrics compared to the extra depth of the stacked DDF data will improve the detectability of objects that are fainter than the WFD limits simulation with 3% survey time (ddf_frac_ddf_- (e.g., Smotherman et al. 2021) and thus either smaller or more per0.6 ), as WFD revisits are particularly important for distant. Four out of five of the DDFs are at ecliptic latitudes light-curve infill and for linking the motion of solar system >15° (Table 6), which means that they can only contain solar objects. However, both options are still within a negligible loss system objects on moderate-to-high-inclination orbits. These margin (<5%) on both metrics when compared to the baseline. objects are comparatively rare (Gladman & Volk 2021; Raymond & Nesvorný 2022), which will result in few observations of solar system objects in these four DDFs. The fifth field, COSMOS, is centered ∼9° from the ecliptic plane “1.6” indicates that the time allocated to DDFs is 1.6 times the baseline (Table 6): this lower latitude makes it sensitive to the mildly value. dynamically excited small body populations, so it is the DDF “0.6” indicates that the time allocated to DDFs is 0.6 times the baseline most likely to be directly beneficial for solar system science. value. 41 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 30. Investigating ways of reducing extra repeat visits and redistributing (v2.1 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. 4.6.2. “Rolling” DDFs the “stripes” of the WFD discussed in Section 4.5, these simulations instead alter the frequency of observation for each From the field population sensitivities, the key aspect of individual DDF and the relative weighting of time between solar system science interest is the choice of rolling cadence for DDFs. They include cases where specific DDFs are observed the COSMOS field and how it affects the small body metrics. only in certain years (e.g., only in the first 3 yr of LSST). The suggested types of DDF rolling cadences explored in the Between v2.1 and v2.2, a large number of DDF strategy v2.1 cadence simulations are unique to the DDFs: in contrast to 42 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. 2 2 Figure 31. Comparison of the sky coverage with greater than 825 deg (in cyan) and 750 deg (in black) for v2.0 cadence simulations with various options for third repeat visits. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. variations were considered, but in general solar system metrics pointings for extended periods of time, longer than other solar have not been produced for these runs. The impacts of system populations. For the LSSTCam FOV, the time in years variations of DDF strategy while keeping the overall envelope for a TNO to pass through the field is given by of allocated DDF time and field location approximately 3. 5 3.5 1.437 constant are expected to produce negligible changes. Once a t = =» ,4() -- 11 24 hr day ´ 365.25 day yr 2.435aa narrower range of DDF strategies are under consideration, solar 3600 system metrics will be produced and checked for potential impacts. Therefore, we consider how DDF “rolling” cadences where α is the opposition on-sky rate of motion in arcseconds would probably affect solar system science, with a specific per hour. For distances of 40–60 au, a TNO traverses the field focus on the highest-yield COSMOS DDF. in ∼5–8 months. In comparison, more distant TNOs (r … 200 The Jupiter Trojans will complete approximately one full orbit au) remain in the field for …2 yr. This means that the during the span of the LSST. The slightly asymmetric populations population of r ∼ 30 au TNOs observed in a DDF is refreshed lead and trail the giant planet in its orbit by ∼60°, with a mean ∼30 times during LSST as a result of (primarily Earth’s) orbital libration amplitude of 33° from the center of their respective motion, compared to r = 300 au TNOs, which would take a Trojan clouds (Marzari et al. 2002). The more populous L4 third of the full survey to pass through the field. COSMOS and, cloud’s inclination distribution is centered around the ecliptic at lower yield, the other DDFs thus provide multimonth TNO latitude of COSMOS, while the flatter L5 inclination distribution orbital arcs that would determine parameters r and i to a still encompasses COSMOS (Slyusarev & Belskaya 2014).The broad Jupiter Trojan libration distribution produces an on-sky precision useful for population studies. However, these arcs are distribution that has wide wings of consistent density around the generally too short to reduce uncertainties on a and e to levels libration centers. These orbital properties mean that Jupiter sufficient for Neptune resonance classification (Volk et al. Trojans will be visible in the COSMOS DDF during distinct 2016). Deep revisits by LSST around the DDFs in later years to several-hundred-day observation periods within LSST recover the DDF-sourced TNOs would be necessary for this (Figure 39). This will permit smaller-diameter Jupiter Trojans to additional improvement for outer solar system science. There- be discovered than can be achieved by the WFD. Therefore, it is fore, TNO science is flexible relative to the DDF “rolling essential that the COSMOS DDF is observed at times when the cadence” decision, as long as COSMOS and other DDFs are Jupiter Trojans are passing through the field. visited for approximately 2 yr at some point within LSST. Throughout the LSST, the COSMOS and other DDFs will provide a constantly refreshing sample of shift-and-stack- 4.7. Microsurveys detectable TNOs smaller than can be seen in single frames of −1 the WFD. As TNOs move slowly (<5″ hr for r > 30 au; see A wide variety of special small observing programs have Figure 25), they will remain in the sidereally static DDF been proposed by the Rubin Observatory user community that 43 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 32. Impact of various third visit scenarios (v2.0 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. We have truncated the y-axis for visibility in the light-curve inversion metrics plot. The fraction of the Jupiter Trojan detections in some of these runs compared to the baseline is lower than 0.4 and off the bottom edge. have been grouped together under the microsurveys category. and provide unique benefits not obtained from the larger Smaller than the minisurveys that have been incorporated into components of the LSST observing strategy. Some of these the LSST footprint, each microsurvey consumes between proposed microsurveys plan to observe new regions of sky not approximately 0.3% and 3% of the total available observing covered within the survey footprint, while others reobserve time. The microsurveys compliment the other components of regions of the sky already covered in the LSST footprint with a the LSST (WFD, DDFs, NES, and Galactic plane observing) separate observing strategy. Of all the proposed microsurveys, 44 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 33. Snapshot of the cumulative number of on-sky visits in all filters as a function of a subset of rolling cadence scenarios simulated at Year 3.5 (v2.0 simulations). The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. the one that is most relevant to the discovery and follow-up of past, Rubin Observatory’s large aperture size would put it in a minor planets and ISOs is the low-SE twilight survey, which unique position to provide a more sensitive search for several aims to take short exposures closer to the Sun in order to search populations of solar system objects such as IEOs, Earth for small bodies in an orbital phase space that the rest of the Trojans, and sungrazing comets than has been performed LSST is not sensitive to. previously (Seaman et al. 2018). NEOs in the region of the solar system interior to Earth’s orbit (including Atiras with orbits interior to the orbit of Earth and “‘Ayló’chaxnims with 4.7.1. Low Solar Elongation Solar System Twilight Microsurvey orbits interior to Venus” orbit ) are the least constrained portion of currently available NEO models owing to observa- The twi_neo family of simulations use 50% of the tional limitations of objects at low SE (Greenstreet et al. 2012; available observing time during morning and evening twilight to perform a microsurvey of the low-SE (40°  SE  60°) sky, which would otherwise not be observed during the WFD Objects on orbits entirely within the orbit of Venus have been previously referred to in the literature as Vatiras (Greenstreet et al. 2012). This name has observing cadence (see Figure 40). The opportunity for LSST acted as a placeholder until the first object in this population is discovered and to observe the low-SE sky during twilight is the only time when named. With the recent discovery and naming of the first inner-Venus object, viewing solar system objects inward to Earth is possible. ‘Ayló’chaxnim (Bolin et al. 2020c, 2022; Ip et al. 2022), we adopt the name of Although surveys similar in nature have been carried out in the this population after its first known member, as is tradition. 45 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 34. Snapshot from a video animation of the baseline_v2.0_10yrs to demonstrate how the two-band rolling cadence observing strategy is implemented over a 10 yr simulated LSST survey. The animation steps through in 30-day intervals over 10 yr, displaying the cumulative number of on-sky visits in all filters (left) and presenting the total number of on-sky visits in all filters accumulated during the time step (right). The animation has a real-time duration of 25 s. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. (An animation of this figure is available.) Figure 35. Snapshot from a video animation of the rolling_ns3_rw0.9_v2.0_10yrs to demonstrate how the three-band rolling cadence observing strategy is implemented over a 10 yr simulated LSST survey. The animation steps through in 30-day intervals over 10 yr, displaying the cumulative number of on-sky visits in all filters (left) and presenting the total number of on-sky visits in all filters accumulated during the time step (right). The animation has a real-time duration of 25 s. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. (An animation of this figure is available.) Granvik et al. 2018). In addition, recent observational evidence orbit determination of the often short-lived visitors (Bolin et al. and dynamical studies suggest that there are possible 2020b; Ye et al. 2020). Low-SE observations of LSST- metastable regions in the innermost portion of the solar system discovered ISOs would provide further opportunities beyond where more objects on orbits similar to that of ‘Ayló’chaxnim what the WFD observing cadence would offer for observing may be lurking and awaiting discovery (de la Fuente Marcos & possible mass shedding, outbursting, or breakup events of these de la Fuente Marcos 2020a; Greenstreet 2020; Popescu et al. interstellar interlopers, as well as extend the amount of time 2020; Bolin et al. 2022, 2023; Ip et al. 2022; Sheppard et al. these short-lived visitors can be observed. Monitoring the sky 2022). in the near-Sun region could also provide the opportunity to In addition to the discovery of IEOs, a LSST low-SE twilight observe cometary outbursting or breakup events as non- microsurvey could enhance the discovery of ISOs; ISO 2I/ interstellar near-Sun comets reach heliocentric distances Borisov was discovered during twilight by an amateur <1 au, which may otherwise not be characterized. Observing astronomer in 2019 (Borisov 2019). Routine observations at comets (with origins from either within our solar system or low SE could also provide prediscovery images of ISOs, interstellar space) as they reach the near-Sun region will better enabling additional astrometric measurements for improved inform us of how insolation can process cometary surfaces and 46 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 36. Snapshot from a video animation of the six_rolling_ns6_rw0.9_v2.0_10yrs to demonstrate how the three-band rolling cadence observing strategy is implemented over a 10 yr simulated LSST survey. The animation steps through in 30-day intervals over 10 yr, displaying the cumulative number of on-sky visits in all filters (left) and presenting the total number of on-sky visits in all filters accumulated during the time step (right). The animation has a real-time duration of 25 s. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°. (An animation of this figure is available.) connect the near-Sun comet population to comets as a whole nights on/four nights off). Figures 41 and 42 show the impact (Seaman et al. 2018). of these low-SE twilight microsurvey cadence options on the Discoveries of PHAs can also be enhanced with the discovery and light-curve inversion solar system MAF metrics. inclusion of a low-SE twilight microsurvey, improving our We note that Figure 41 provides discovery completeness values knowledge of and increasing warning times for potential that are not normalized to the baseline simulation’s output since asteroid impacts. In addition, the possible discovery of more there are no ‘Ayló’chaxnims discovered with the baseline Earth Trojans, which librate at the Earth–Sun L4 and L5 survey cadence; this is the only figure that shows the outcomes Lagrange points, would improve our knowledge of planetary of the MAF metrics analysis that is not normalized to the impactor sources for both recent and ancient cratering events baseline simulation’s output. on Earth and the Moon (Seaman et al. 2018; Malhotra 2019; The discovery completeness for the ‘Ayló’chaxnim popula- Markwardt et al. 2020). Earth Trojans also make attractive tion (NEOs with orbits interior to the orbit of Venus) for a spacecraft mission targets owing to their low relative velocity variety of cadence options for this microsurvey are compared to with Earth. Lastly, observing asteroids in the near-Sun region that for the baseline survey (with no low-SE twilight with LSST can provide the opportunity to probe mechanisms microsurvey) in Figure 41. Each of the microsurvey cadence responsible for the supercatastrophic disruption of asteroids options provides a (sometimes much) higher discovery with small perihelia (closest orbital distance to the Sun; completeness than the baseline survey, which does not include Granvik et al. 2016) and test the extent to which this any ‘Ayló’chaxnim discoveries since ‘Ayló’chaxnims are only phenomenon occurs for asteroids that reach very small solar visible at SEs smaller than the WFD cadence reaches. In the top distances. panel of Figure 41, which uses the Rubin SSP discovery criteria Due to the large amount of science for a wide variety of of three nightly pairs in 14 days, the highest ‘Ayló’chaxnim small body populations that would be made possible with a discovery completeness is reached when the low-SE twilight low-SE twilight microsurvey, a family of runs executing a microsurvey is run every night with three repeat visits per variety of low-SE observing cadences during twilight have pointing in either iz or riz filters (i.e., twi_neo_repea- been included in the last few rounds of cadence simulations, the t3_iz_np1_v2.2_10yrs and twi_neo_repea- most recent of which are the v2.2 simulations. The v2.2 family t3_riz_np1_v2.2_10yrs). These microsurvey cadences is split into twi_neo and twi_neo_brightest, which would result in the completeness of the H „ 20.5 and the execute the minisurvey when the Sun is above −17°.8 and H „ 16.0 ‘Ayló’chaxnims increasing to ≈1.5% and ≈2%, −14° elevation, respectively. The twi_neo and twi_neo_- respectively, providing the potential for a significant increase in brightest simulations consist of 15 s exposures per visit the discovery of inner-Venus asteroids. Running the micro- and explore a variety of repeat visits, filters, and nightly survey with either three or four repeat visits during either the twilight on-off cadences. The twi_neo_repeatX_Y_npZ_- brightest twilight time (i.e., when the Sun is above −14° v2.2_10yrs and twi_neo_brightest_repeat- elevation) or full twilight time (i.e., when the Sun is above X_Y_npZ_v2.2_10yrs families consist of X repeat visits ( −17°.8 elevation) produces similar results in ‘Ayló’chaxnim i.e., triplets or quads; all separated by ∼3 minutes) in Y filter(s) discovery completeness across the various nightly “on”/“off” per pointing per twilight observed where Z = 1 (on every cadences. The largest discovery completeness increases for night),2 (one night on/one night off),3 (one night on/two these options occur when the microsurvey is run every night nights off),4 (one night on/three nights off),5 (four nights using either iz or riz filters, which result in increases of ≈1% to on/four nights off),6 (three nights on/four nights off),7 (two ≈1.5%. Simply using the z filter does not get as large of a 47 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 37. Impact of various rolling cadence scenarios (v2.0 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. The baseline simulation has a two-band rolling cadence implemented with no rolling in the Galactic plane and NES. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. Note: The light-curve inversion plot has been truncated for clarity. The Jupiter Trojans extend beyond the plot for the six_rolling_ns6_rw0.9_v2.0_10yrs. discovery boost as using either iz or riz filters. Unsurprisingly, If, unlike the Rubin SSP requirement of three nightly pairs in 14 days, four detections in a single night with four repeat visits the less often the microsurvey is run (fewer number of “on” per pointing are required, the discovery completeness improves nights), the lower the ‘Ayló’chaxnim discovery completeness further. This is more typical of observing cadences used for drops. 48 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 6 every night is completely detrimental, with up to 40% drops for Planned LSST Deep Drilling Fields H „ 15 Jupiter Trojans and 20% drops for H „ 18 MBAs compared to the baseline survey that does not include any low- Deep Drilling Field R.A. Decl. Ecliptic SE twilight microsurvey. Of the microsurvey options discussed (deg)(deg) Latitude above, the only option that does not drop the fraction of objects (J2000)(J2000)(deg) for which light-curve inversions can be obtained by >5% from ELAISS1 (European Large-Area ISO Sur- 9.450 −44.00 −43.18 that of the baseline survey (the level considered acceptable) vey-S1) field is twi_neo_repeat4_riz_np4_v2.2_10yrs, which XMM-LSS (X-ray Multi-Mirror Mission- 35.71 −4.75 −17.90 includes four repeat visits (i.e., quads) per pointing in riz Newton Large Scale Structure) field ECDFS (Extended Chandra Deep Field 53.13 −28.10 −45.47 filters, where the low-SE twilight microsurvey is run with a South) cadence of one night on/three nights off. This simulation keeps EDF-S (Euclid Deep Field South) 61.24 −48.42 −66.60 light-curve inversion at baseline level or above with up to a COSMOS (Cosmological Evolution Sur- 150.10 2.18 −9.40 30% increase above baseline levels for H „ 16 PHAs. As vey) field described above, this option also performs well for discovery completeness, which provides baseline-level performance or higher (up to ≈2%) for all included small body populations NEO discovery by current surveys (e.g., Gehrels & except the H „ 12 OCCs with a maximum perihelia of 20 au, Jedicke 1996; Larson et al. 2003; Tonry et al. 2018) Such a which again get a ∼0.5% discovery completeness drop. An cadence would require the development and implementation of additional benefit to having four repeat visits instead of three code outside SSP, which is designed only to use image pairs to repeat visits is better resiliency to contamination by satellites make tracklets. Under this alternative cadence, running the streaks (for additional discussion, see Section 5.4). microsurvey every night during the brightest part of twilight in In general, running the low-SE twilight microsurvey less either iz or riz (i.e., twi_neo_brightest_repea- frequently proves better for both discovery completeness and t4_iz_np1_v2.2_10yrs and twi_neo_brightes- light-curve inversion when all solar system small body t_repeat4_riz_np1_v2.2_10yrs in the bottom panel of populations are considered. Furthermore, running the low-SE Figure 41), the discovery completeness increases to ≈8% and twilight microsurvey at an infrequent cadence boosts discovery ≈9.5% for the H „ 20.5 and H „ 16.0 ‘Ayló’chaxnims, completeness overall, including for the ‘Ayló’chaxnims, which respectively. Given that the baseline ‘Ayló’chaxnim discovery also see a significant discovery completeness enhancement in completeness is zero, a low-SE twilight microsurvey thus has both discussed simulations (to ≈0.5%–0.75% for three repeat the potential for a dramatic shift in the discovery of asteroids visits in riz with a cadence of three nights on/four nights off or interior to the orbit of Venus. ≈0.15%–0.25% for four repeat visits in riz with a cadence of In contrast, Figure 42 (top panel) shows that for nearly all one night on/three nights off). Light-curve inversions are also other solar system small body populations, running the low-SE enhanced when the low-SE twilight microsurvey is run twilight microsurvey every night produces the largest drops infrequently (once every 3 days with four repeat visits per (≈3.5%) in discovery completeness, in particular for the fainter pointing in riz filters) compared to the baseline survey cadence. objects. This is because the low-SE twilight microsurvey takes Given these enhancements and the large amount of science for time away from the WFD observing that would otherwise be a wide variety of small body populations that would be made performed during those twilight hours. This produces a drop in possible with a low-SE twilight microsurvey, we thus strongly the discovery completeness for faint objects that would encourage an infrequently run low-SE twilight microsurvey to otherwise be discovered at larger SEs; faint objects are also be included in the LSST survey cadence from the start of the harder to see than brighter objects when looking near the Sun. survey. We note for the reader that the significant increases This drop is also increased when the microsurvey observations shown here from the discovery metrics when the twilight low- are only made with the z filter. On the other hand, bright SE microsurvey is included will not translate into the exact (H„ 16) NEOs and PHAs get the strongest discovery boost same gains in the actual LSST ‘Ayló’chaxnim, H „ 16 NEO, when the microsurvey is run every night since more of the easily and H „ 16 PHA discovery yields. It depends on the size and visible objects are picked up in the additional sky coverage. Of albedo distribution of these populations, which is not included all the twi_neo family simulations, the two best options for in the calculation of our discovery metrics (see Section 2.3.1). discovery completeness for all included small body populations The significant increase in our metrics does show that including are twi_neo_repeat3_riz_np6_v2.2_10yrs,which the microsurvey will significantly enhance LSST’s chances of includes three repeat visits (i.e., triplets) per pointing in riz finding new‘Ayló’chaxnims and other IEOs, but the actual filters where the low-SE twilight microsurvey is run with a number of new discoveries may be very small. cadence of three nights on/four nights off, and twi_neo_r- Given the numerous scientific benefits and enhancements in epeat4_riz_np4_v2.2_10yrs, which includes four repeat solar system discoveries and light-curve inversions that will visits (i.e., quads) per pointing in riz filters with a cadence of one come from running the low-SE twilight microsurvey, as night on/three nights off. In these simulations, the only described above, we recommend avoiding waiting to start this population to see a drop in discovery completeness are the microsurvey until Year 2 or later in the 10 yr survey. One H„ 12 OCCs with a maximum perihelia of 20 au, which get a additional reason for starting this microsurvey in Year 1 is the ∼0.5% discovery drop; all other populations either match the increasing number of satellite constellations being sent into low baseline or gain an increased discovery completeness (up to ∼3.5% and ∼2%, respectively). Earth orbit. These satellite constellations are most problematic When considering the ability to perform light-curve inver- for astronomic observations during twilight hours, when the sions for PHAs, NEOs, MBAs, and Jupiter Trojans (bottom numerous satellites are brightest in the sky (for further panel of Figure 42), running the low-SE twilight microsurvey discussion of the impact of satellite constellations on solar 49 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 38. Impact of varying the time allocated to the DDFs (v2.0 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. system science, see Section 5.4). With the number of satellite of satellite contamination as much as possible and enable the constellations continuing to increase, and plans for that increase most solar system science. The SCOC has recently made a to continue for years to come, the problem of contamination recommendation for a low-SE NEO twilight microsurvey to be will only get worse during the later years of the 10 yr LSST included in the survey strategy starting in Year 1 of LSST, with survey. We thus recommend starting the low-SE twilight further opportunities to explore the final details of the microsurvey in Year 1 of operations in order to reduce the level implementation (Bianco & the SCOC 2022). 50 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. within days of discovery and not require explicit self-follow-up by Rubin. It would then be interesting to examine this scenario in further detail (since it would not follow the traditional SSP detection method) and quantify the number and purity of high digest 2 tracklets (i.e., short-arc moving object detections likely to be solar system objects) Rubin would identify and report on a nightly basis, as well as the typical apparent magnitude of reported tracklets (i.e., assess whether the broader community’s NEO follow-up system would be able to keep up with this modified reporting method). 4.7.2. Other Microsurveys A number of additional microsurveys requiring 0.3%–3% of Figure 39. The number of Jupiter Trojan detections (of a simulated sample overall survey time were submitted in response to the 2018 population of 5000 objects) in the COSMOS DDF over the course of the baseline_v2.2_10yrs simulation. This simulation starts the LSST in white paper call on survey strategy. These microsurveys 2023 October. By 7 yr into the survey, both Jupiter Trojan clouds have include the following: traversed across the COSMOS DDF. 1. Adding extra fields: virgo (adds the Virgo Cluster −1 to WFD), carina (1 week yr in Carina Nebula), smc_movie (short g exposures in SMC for two nights), roman (covering the Roman bulge field twice per year). 2. local_gal_bindx ⟨I⟩: Covers 12 Local Group galaxies with extra gri exposures. 3. too_rate ⟨X⟩: Follows Targets of Opportunity, where X is the number per year, 4. north_stripe: Adds northern extension stripe up to decl. +30 (illustrated in the final panel of Figure 2). 5. short_exp: Takes up to 3 × 5 s exposures in Year 1 of the survey. 6. multi_short: Takes sequences of 4 × 5 s exposures in each filter, with the aim of obtaining 12 total sequences of short exposures per year, achieving ∼700 exposures per pointing. As shown in Figure 43, the effects of the majority of these Figure 40. Comparison of the number of visits as a function of SE (at the microsurveys on the discovery and light-curve metrics that are center of the FOV) with and without a low-SE solar system twilight of most concern for solar system science are minimal (5%) microsurvey. The bin size is 2°. The baseline simulation is baseline_- since this involves a very small fraction of the overall LSST v2.2_10yrs, and the selected low-SE solar system twilight microsurvey survey time. The exception to these generally minimal effects is simulation is twi_neo_repeat4_iz_np1_v2.2_10yrs. We note that part of the orange histogram for the simulation that includes the low-SE seen in the multi_short simulation (final column in twilight microsurvey is plotted underneath the blue histogram for the baseline Figure 43). This survey strategy causes a ∼5%–25% drop in simulation. the number of objects detected, particularly for the fainter solar system object populations (this outcome is to be expected with Finally, we look at possible further cadence enhancements the switch of 12 of the ∼82 exposures per pointing per year to and/or software improvements. All currently analyzed much shorter exposures). While the vast majority of the cadences assume either three or four repeat visits (i.e., triplets additional microsurveys have little to no effect on the metrics or quads), but the discovery criteria are the same as used for of most interest to solar system researchers, there remains the WFD observations (linking three tracklets over a 14-day possibility that the combination of several of these micro- period). Under such assumptions, the standard observing surveys could “constructively interfere” in such a way as to strategy of requiring pairs should be examined as well. Hints cause a large impact later. However, these microsurveys also that it may perform better are in Figure 41: note how repeat3 only impact a very small amount of the survey time and the cadences systematically produce more discoveries than overall survey strategy, and so the decision on the details of repeat4; a hypothetical repeat2 cadence may even further microsurveys may well also be delayed until later in the increase our sensitivity to ‘Ayló’chaxnims. We recommend cadence decision process. A combined set of microsurveys will that such cadences be simulated and analyzed. Should the analysis conclude that three- or four-tracklet likely be the subject of further simulation runs later when the cadences are still preferred, we would recommend that the larger and more influential parts of the cadence strategy are Rubin Observatory consider reporting such tracklets to the decided on. MPC immediately (within 24 hr). This would allow for third- party follow-up of such objects, which may be few enough and interesting enough that it is feasible that they may be followed https://www.lsst.org/submitted-whitepaper-2018 51 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 41. ‘Ayló’chaxnim (previously known in the literature as Vatira) population discovery completeness comparisons for cadences that include a possible low-SE solar system twilight microsurvey (v2.2 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. These metric values have not been normalized to the baseline’s simulation output since the baseline simulation, which does not include the low- SE twilight microsurvey, finds no ‘Ayló’chaxnims with three nightly pairs over 14 days. Top: discovery completeness for three nightly pairs in 14 days (the Rubin SSP discovery criteria). Bottom: discovery completeness for four detections in a single night for twilight simulations that take four visits per pointing. Simulation legend: twi_neo_repeatX_Y_npZ_v2.2_10yrs for a microsurvey with X repeat visits in Y filter(s) per pointing per twilight observed where Z = 1 (on every night),2 (one night on/one night off),3 (one night on/two nights off),4 (one night on/three nights off),5 (four nights on/four nights off),6 (three nights on/four nights off),7 (two nights on/four nights off). returns that are not yet captured in these simulations. The 5. Additional Considerations Not Explored in the Cadence survey cadence simulations do not account for the difference in Simulations Rubin Observatory operations in the first year of the survey The LSST cadence simulations are incredible tools for compared to later years. The growth of low-Earth-orbit satellite exploring the wide range of scenarios for how Rubin constellations and their future impact on LSSTCam observa- Observatory can survey the sky, but there are a few additional tions is not yet quantified. Targeted small observing programs potential factors that could enhance or impact the LSST science that take much less than 1% of the LSST observing time are not 52 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 42. Impact on other solar system metrics due to the inclusion of a low-SE solar system twilight microsurvey (v2.2 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. The baseline simulation does not include twilight low-SE observations. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. Simulation legend: twi_neo_repeatX_Y_npZ_v2.2_10yrs for microsurvey with X repeat visits in Y filter(s) per pointing per twilight observed where Z = 1 (on every night),2 (one night on/one night off),3 (one night on/two nights off),4 (one night on/three nights off),5 (four nights on/four nights off),6 (three nights on/four nights off),7 (two nights on/four nights off). commissioning of LSSTCam and the Simonyi Survey included in the cadence simulations, and opportunities to Telescope. propose to Rubin Observatory with these very small observing requests will be considered closer to the start of the survey 5.1. Incremental Template Generation in Year 1 (Ivezić & the SCOC 2021). Additionally, the combined benefits of the LSST data with future wide-field surveys The LSST cadence simulations and the MAF metrics assume cannot be derived from the cadence simulations alone. Some of that the first year of the survey will run exactly like later years, these considerations require analysis of test observations during but this is not quite the case. The data management pipelines 53 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Figure 43. Impact of various microsurvey scenarios. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. The y-axis is truncated in the bottom plot at 1.6 for readability. The light-curve inversion NEO H = 19.0 and light-curve inversion PHA H = 19.0 extend to nearly 2.5 for the multi_short_v2.0_10yrs run. use difference imaging to identify transient sources within the template for the observed field must exist in the given filter of LSST exposures by subtracting off a template representing the the observation. Templates are expected to be produced during static sky. The Rubin SSP pipelines use these catalogs nightly the data processing of the yearly data release. A brief overview to identify moving sources as part of the prompt products data of how templates are likely going to be made from coadded processing (Jurić et al. 2021). In order for new solar system observations is available in the summary paper describing the objects to be discovered in real time during the survey, a LSST DESC DC2 (Dark Energy Science Collaboration second 54 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. data challenge) simulated sky survey (LSST Dark Energy on model orbits (Trilling et al. 2018a). Objects at these size Science Collaboration (LSST DESC) et al. 2021). Some fields ranges in the outer regions of the solar system are particularly will have enough observations in commissioning to generate underobserved by current surveys owing to their faint apparent templates at the start of the survey, but this will not be true for magnitudes, and new constraints from Rubin Observatory the vast majority of the sky. Year 1 of the LSST will have to be would provide vital information on planetary formation and treated differently if solar system bodies are to be detected collisional processes that have occurred and still occur in our nightly. Otherwise, these discoveries will only be made during solar system. An additional set of four return visits to these the data release processing to make the yearly detection deep fields over a 2 yr period is proposed that would enable catalogs from LSST only data. dynamical classification and color measurements (if one of the Within MAF there are no solar system metrics focused on later visits was taken in a different filter). In addition to the Rubin prompt data products. Whether or not a different exploring the nature of the observed TNO size distribution and template generation strategy is used in Year 1 of the LSST, the the physical properties of the TNOs on both sides of the broken total number of discoveries and the number of objects with power law, the proposed solar system DDFs would also sufficient observations for shape inversion will remain the provide further characterization of other solar system objects. same. These metrics only probe what would be available in the Densely sampled light curves of MBAs, Centaurs, and Jupiter yearly data release catalogs at the end of the 10 yr span of the Trojans can reveal any temporal variability in color and LSST. They do not quantify the impact on the study of brightness within the 2 hr observation period. From this, transient phenomena. If no templates are produced in the physical properties such as color, size, and shape can be LSST’s first year, all time-domain-related events (such as ISO constrained. apparitions, NEO/PHA close fly-bys, and cometary outbursts) The total program requires 40 hr of observing time over the present in the first year of survey observations would be 10 yr of the LSST (totaling =0.3% of total survey time), the discovered 6 months to 1 yr after they occurred. The duration equivalent of four winter observing nights. This request is well of these events is on the scale of days to weeks; discovering below the threshold for cadence variations to be evaluated by these events at the time of the first and second LSST data the SCOC and therefore has not been included in the LSST release would be too late to perform any additional observa- cadence simulation runs. We highlight this proposed program tional follow-up (such as obtaining spectra) with other here, as it would deliver unique science not achieved with the facilities. Schwamb et al. (2021) highlight in further detail TNO sample found in the WFD, NES, or DDFs. The SCOC some of the unique opportunities for solar system science has recommended to the Rubin Observatory Operations Team enabled in the first year of the LSST if templates are generated that a very small amount of survey time be allocated in a call and implemented into Rubin Observatory’s data management for proposals for observing requests at this scale once the pipelines. survey performance has been evaluated (Ivezić & the The Rubin Observatory Operations Team has committed to SCOC 2021). producing templates incrementally during the first year of the LSST (Guy et al. 2021), but the exact requirements for 5.3. Euclid Synergies producing these Year 1 templates have not yet been decided. The specific strategy used will impact which observations SSP The Euclid Deep Field South is the fifth DDF to be adopted can search before the data release 1 processing and to what into the LSST after it was proposed in a written response to the limiting magnitude. It will also impact the productivity of the 2018 LSST Cadence Optimization White Paper Call (Capak low-SE solar system twilight microsurvey (described in et al. 2019a). This DDF will overlap with the southern deep Section 4.7.1), if the SCOC recommends the microsurvey to field observed as part of the upcoming ESA Euclid mission be included in the LSST year 1 observing strategy. The (Laureijs et al. 2011; Amiaux et al. 2012). Euclid aims to map microsurvey gains most benefit if astrometric follow-up the geometry of the dark universe during its 6 yr visible and observations can be performed by other observing facilities in near-infrared photometric and spectroscopic survey and will tandem with the LSST observations. Exploring the implications provide complementary observations to LSST’s wide-field of various incremental template generation strategies is beyond visible ground survey for a number of the LSST science goals. the scope of this paper, but this analysis should be carried out While only solar system objects with ecliptic latitudes beyond before the end of Rubin Observatory’s commissioning period. ±15° will be observed by Euclid, the science returns from its near-infrared capabilities, high angular resolution, and densely sampled light curves will still be significant (Carry 2018). 5.2. Solar System Deep Fields Previous studies suggest that the entire combined Rubin-Euclid Trilling et al. (2018a) proposed dedicated solar system data set would allow for the spectral classification of roughly “DDFs” in response to the 2018 LSST Cadence Optimization 150,000 solar system objects largely unknown to date and White Paper Call. This observing program was different in provide constraints on shape, rotation, activity, and binarity for scope than the typical DDFs currently imagined as part of a significant number of asteroids, Centaurs, and TNOs LSST and described in Section 4.6. Thus, we will refer to these (Carry 2018; Snodgrass et al. 2018; Guy et al. 2022).In pointings as solar system Deep Fields instead. This proposed particular, contemporaneous observations from both Euclid and program would consist of five different pointings at a range of LSST will allow rapid determination of orbits, which is vital for ecliptic latitudes, including coverage of parts of the leading and the recovery and follow-up of rare solar system objects trailing Neptune Trojan clouds. Each solar system deep field (Snodgrass et al. 2018). For more details on proposed Rubin- would be observed for 2.1 continuous hours in a single filter to Euclid derived data products see Guy et al. (2022). While reach the image depth (r = 27.5 mag; 3 mag deeper than the LSST can and will adapt its nightly observation schedule to WFD + NES observations) required to observe TNOs as small local weather conditions, the cadence and pointings of Euclid as 25 km in diameter through shifting and stacking the images are fixed and, therefore, known well in advance. As such, it 55 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. would fall to LSST to optimize its nightly schedule to surface brightness into sky that may later be the source of maximize the number of near-simultaneous pointings with detections when images are stacked; and they reduce the Euclid. Although an initial assessment of the simultaneous dynamic range available for the lower-S/N solar system astrometry between LSST and Euclid has been performed and detections. presented at the Rubin Project and Community Workshop in A recent study by Hu et al. (2022) finds that ∼10% of all 2019, no simulations currently exist that would quantify the LSST images will have a streak from a launched or planned-to- impact of newer LSST cadence simulations with respect to be-launched Starlink (Generation 1 or 2) or OneWeb satellite, joint Euclid-Rubin solar system science. with observations in twilight more frequently impacted. If significantly more satellites are launched over the next several years, more LSST observations could have streaks. The effects 5.4. Unquantified Considerations: Satellite Constellations of satellite constellations have not yet been comprehensively It is now certain that the accelerating industrialization of quantified for Rubin Observatory’s solar system science, and near-Earth space will have major adverse effects on astronom- we do not attempt to do so here, as no cadence simulations yet ical observation (Walker et al. 2020a, 2020b, 2021; Hall et al. include impacts from these satellite streaks on LSSTCam: we 2021; Halferty et al. 2022). The future density of global merely highlight a number of potential projected loss effects. satellite constellations is as yet uncertain, as it depends on First, the shallowing of LSST will decrease the detected solar commercial and regulatory decisions. However, regulatory system populations across any and all cadences. Across all approval has been granted by the United States for at least populations, detection loss for individual objects will deplete 30,000 satellites (Walker et al. 2021); >2500 of these launched the sparse light curves LSST generates, for instance, limiting in the past 3 yr, with 212 Starlinks in 2022 May alone. This the ability to detect small body activity (Section 2.3.2). Second, means that Rubin Observatory will have to observe into a there will be population-dependent losses in solar system hyperindustrialized sky. The first few years of LSST will science from satellite effects. In particular, for detections of contain the effects of the iterative passes of at least 6000 low- NEOs, which individually only become visible for a small Earth-orbit satellites—and as constellation build-out continues, subset of time within the span of LSST, the steep size satellite density will only increase throughout the survey. distribution means that the majority of detections are made For LSSTCam, there are notable streak effects when toward the Rubin magnitude limit. NEO discovery by the SSP illuminated satellites cross the focal plane during an LSST pipelines requires a pair of detections (see tracklets) and is thus exposure (Tyson et al. 2020). The level to which a streak could fragile to satellite effects: losing single detections from a pair be partly or fully saturated in the LSST images depends on has a disproportionate impact on the detectability of this each satellite’s orbit, morphology, reflectance properties, and population, as for intranight cadence outcomes (see orientation; the severity can vary through time, such as when a Sections 4.4.1, 4.4.3, and 4.4.4). Similarly, satellite-generated single launch’s “train” of co-released satellites are on its orbit detection loss will also acutely affect solar system populations raise and are brighter than when on final orbit. In these cases, that are only visible for week-to-month time periods, such as and also when the satellite is fainter and so the streaks are not ISOs and newly active comets. The seasonality of satellite saturated, the impacted pixels will not be suitable for density—more satellites are illuminated, and for longer, in photometry: each satellite streak decreases the effective sky summer—will have a seasonal impact on solar system object coverage of the exposure. Satellites at m ; 7, with bright- detectability (Hainaut & Williams 2020; McDowell 2020; nesses below saturation though at S/N; 100, are also Lawler et al. 2022). Seasonality detection biases adversely anticipated to create substantial multiorder cross talk. These affect all solar system populations that cluster on parts of the highly correlated linear “ghost” streaks center on cores sky (e.g., Trojan populations, resonant TNOs, potentially the surrounded by wings several hundred pixels in extent. The high-q, high-a TNOs, NEOs, and ISOs); they require careful degree to which Rubin Observatory’s processing pipelines will debiasing to generate accurate population models (e.g., be able to mask is yet to be determined. Even if algorithms can Kavelaars et al. 2020). While not quantified for satellites, be developed for adequate cross-talk removal, spatially general outcomes of inducing this type of effect can be correlated noise will still generate systematics throughout the considered in the vary_NES cadence family (Section 4.1.3). entire LSST data set (Tyson et al. 2020). Additionally, there is Additionally, twilight-bright satellites will be abundant in the an increase in global sky brightness from the ensemble of the low-SE sky that is being targeted for detection of PHAs, IEOs, size distribution of space debris and satellites—which has gravitationally focused ISOs, and comet comae. Illuminated already raised the diffuse zenith luminance by 10% as of 2021 satellites are most numerous near the horizon close to dawn and (Kocifaj et al. 2021). While the community focus to date has dusk; the low-SE twilight microsurvey runs in −12° solar been on the direct solar illumination of Earth-orbiting objects, elevation and darker. For the low-SE twilight microsurvey satellites also reflect moonlight moonshine, and potential (Section 4.7.1), the effects of satellite constellations will be additional sources of illumination (e.g., earthshine, mutual particularly pronounced, with some 90% of images expected to reflectance among illuminated space objects) have yet to be be impacted with at least one streak per image (Hu et al. 2022). modeled in published studies. The industrially caused loss of It may be possible for the Rubin Observatory scheduler to global darkness will continue as more anthropogenic material is selectively observe specific pointings on the sky, which could added to Earth’s orbital environment. For solar system science, decrease the number of WFD images with satellite streaks by a satellites and associated debris will thus have three main factor of two. However, this would come at the substantive cost of effects. They obliterate, or modify in unquantified ways, the photometry of individual object detections that are blazed over ∼10% of the LSST observing time (Hu et al. 2022). The trade- by streak footprints; they introduce systematic errors at low offs of implementing this algorithm would depend on the impact of the satellite streaks on LSSTCam, which, as noted above, has https://www.planet4589.org/space/stats/star/starstats.html yet to be fully characterized, and the number, sky distribution, and 56 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. apparent magnitude of satellites at the start of Rubin science 9. Similarly, increasing the number of exposures in bluer operations. Overall, the characterization of the impacts of satellite filters (u and g) decreases the number of faint objects for constellations on the LSST cadences, given the ever-changing which light-curve inversion is possible. parameters of constellation design, replenishment, and dynamic 10. Restricting repeat nightly visits to the same filter does not operation, will prove challenging. It will require major effort to significantly improve solar system metrics over mixed- model and incorporate into future LSST survey simulations. filter pairs. 11. Including a third visit of a field in the same night can have a very serious effect on the coverage area and other 6. Conclusions solar system metrics, particularly if the “Presto-Color” (rapid color; Bianco et al. 2019) strategy is implemented. By analyzing the LSST cadence simulations and the outputs for Adding the third visit in the same color as the earlier pair a suite of MAF metrics, we have explored the impact on solar and increasing the gap from the initial pair is shown to system science for a wide range of potential LSST cadences. Our have a much lower impact. resulting analysis highlights the importance of simulating the 12. Having the Rubin scheduler better balance extra nightly expected small body detections for future multipurpose wide-field visits beyond pairs in a given night by redistributing them surveys. This allows for tensions between main science drivers to across the sky has some benefit to small body discovery be identified in order to optimize the on-sky observing and with typically small hits to light-curve characterization. maximize the output from next-generation astronomical surveys 13. Rolling cadence strategies are generally positive for solar and facilities. We hope that this paper and the entire focus issue system metrics, although the most extreme rolling that this paper contributes to may serve as resources for future patterns (many stripes or very strong rolling) should be SCOC reviews of the LSST cadence, as well as for future wide- avoided. A rolling pattern that ensures a minimum field survey design. coverage to enable discovery of rare types of objects in In general, we find that a wide range of LSST survey the “off” stripes should be considered. strategies provide satisfactory temporal and spatial coverage to 14. Spending 3%–8% of the survey time on DDF observations achieve the goals for solar system science outlined in the SSSC produces only minimal losses for solar system science. If Science Roadmap (Schwamb et al. 2018a). Below, we some DDFs are observed only in certain years, observing summarize our key findings and recommendations based on the version 1.5–2.2 LSST cadence simulations: the COSMOS DDF for at least 2 yr would be the most beneficial for the detection and orbit characterization of 1. Observing the northern regions of the ecliptic up to +10° small bodies discovered by shift-and-stack algorithms. ecliptic latitude (the NES) is crucial for outer solar system 15. The COSMOS DDF should be observed when the Jupiter science and probing the solar system small body popula- Trojans are passing through the field, which occurs in tions that are asymmetrically distributed on the sky. discrete windows during LSST. 2. Covering the NES to at least 25% of the WFD level is 16. Starting a low-SE twilight microsurvey in Year 1 of critical for discovering and characterizing slowly moving operations would make Rubin Observatory uniquely objects (e.g., TNOs) and faint inner solar system objects. sensitive to several populations of solar system small 3. Shifting time away from the WFD to the Galactic plane bodies such as IEOs, Earth Trojans, and sungrazing can negatively impact light-curve measurements of faint comets and give the LSST the potential to improve solar system objects. asteroid models, test the theory of asteroid supercatas- 4. Shifting the WFD footprint northward from high- trophic disruption at small perihelion distances, and extinction regions to low-extinction sky, such that part improve asteroid impact warning times. An infrequently of the NES region obtains visits at WFD cadence, is a run (e.g., every three nights) low-SE twilight microsurvey welcome change. The new expanded northern WFD + would also enhance small body discovery and light-curve NES footprint used in the v2.0–v2.2 cadence simulations inversion and enable the discovery of ‘Ayló’chaxnims, is conducive to solar system science. which are only visible during twilight. 5. We advocate for moving from 2 × 15 s snaps to a single 17. The vast majority of the additional microsurveys for 1 × 30 s exposure per visit owing to the resulting ∼8% specific regions of sky have little to no effect on the solar boost in on-sky visits. system metrics, but there remains the possibility that 6. Aiming for 33-minute separations between nightly pairs combining several of these microsurveys could produce a is an ideal compromise between achieving a high pair result that causes a large impact later. This will likely completion rate per night and for the Rubin SSP pipelines need to be the subject of further simulation runs later to be sensitive to moving objects at distances up to when the larger and more influential parts of the cadence ∼150 au. strategy are decided on and actual operational overheads 7. Shorter exposure times are beneficial for the discovery of are measured from commissioning activities. PHAs and NEOs, while longer exposures are better for 18. The production of incremental templates in the first year the discovery of more distant objects. Shorter exposures of the LSST is particularly important for the timely also increase the total number of on-sky visits per follow-up of ISO apparitions and other transient solar pointing, providing denser sampling for light-curve system phenomena. Further work is needed to explore the inversion. A compromise between discovery and color/ light-curve characterization is to use 30 s exposures per impact on Year 1 discovery rates for the different visit when possible. potential options for creating the incremental templates. 8. Longer u-band exposures (50 s and above) tend to reduce 19. A 40 hr program as described in Trilling et al. (2018a) to discoveries at small sizes (fainter H) and are detrimental observe a set of solar system Deep Fields would probe the to light-curve inversions. small size end distribution of the TNO and Neptune 57 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Trojan populations that cannot be achieved with the Discovery Fellowships from New Zealand Government funding, currently planned LSST. administered by the Royal Society Te Apārangi. M.S.K. was 20. Creating joint data products with other contemporaneous supported by the NASA Solar System Observations program surveys such as ESA’s Euclid would be of great scientific (80NSSC20K0673). H.W.L. is supported by NASA grant benefit to the solar system science community. Apart NNX17AF21G and by NSF grant AST-2009096. T.D. acknowl- from overlapping DDFs, which are already planned, we edges support from the LSSTC Catalyst Fellowship awarded by suggest synchronizing observations of survey fields LSST Corporation with funding from the John Templeton where possible when choosing LSST nightly cadences. Foundation grant ID No. 62192. S.G. acknowledges support 21. The impact of rapidly increasing industrial activity in near- from the DIRAC Institute in the Department of Astronomy at the Earth space is not modeled here, but the projected adverse University of Washington. The DIRAC Institute is supported effects are substantial. We advocate for careful character- through generous gifts from the Charles and Lisa Simonyi Fund ization of the anthropogenic impacts on the LSST. for Arts and Sciences and the Washington Research Foundation. S.G. also acknowledges support from the Preparing for Astro- Our analysis has focused on the individual impact of changing physics with LSST Program funded by the Heising Simons at the same time one or two observing constraints or parameters Foundation (grant 2021–2975),from NSF (grant OAC-1934752), within the Rubin scheduler. We have not explored the impact of and from NASA (grant 80NSSC22K0978). The work of S.R.C. changing all these parameters simultaneously. We note that was conducted at the Jet Propulsion Laboratory, California although tuning individual knobs for the LSST survey strategy by Institute of Technology, under a contract with the National themselves may have little effect, the combination of several of Aeronautics and Space Administration. R.C.D. acknowledges them may not. This should be carefully considered by theSCOC support from the UC Doctoral Scholarship and Canterbury when they finalize their recommendation for the initial LSST Scholarship administered by the University of Canterbury, a PhD observing cadence. The analysis presented here should be research scholarship awarded through M.T.B.’s Rutherford repeated with the finalized LSST SCOC recommended observing Discovery Fellowship grant, and an LSSTC Enabling Science strategy when it becomes publicly available. Future cadence grant awarded by LSST Corporation. R.M. acknowledges support simulations should be generated and studied to examine additional from NSF (AST-1824869) and NASA (80NSSC19K0785).L.I. options for the low-SE twilight microsurvey. Further investigation acknowledges support from the Italian Space Agency (ASI) is also needed to explore how the various options for incremental within the ASI-INAF agreements I/024/12/0 and 2020-4-HH.0. template generation will impact the potential for real-time This material or work is supported in part by the National discovery and follow-up of our solar systemʼs minor planets Science Foundation through Cooperative Agreement AST- andISOsinthe first year of the LSST. This will be particularly 1258333 and Cooperative Support Agreement AST1836783 important for assessing whether the low-SE twilight survey should managed by the Association of Universities for Research in be included as part of the Year 1 LSST observing strategy. Astronomy (AURA) and the Department of Energy under contract No. DE-AC02-76SF00515 with the SLAC National The authors wish to acknowledge all of the essential workers Accelerator Laboratory managed by Stanford University. who have put their health at risk since the start of the COVID- For the purpose of open access, the author has applied a 19 global pandemic and the researchers who worked tirelessly Creative Commons Attribution (CC BY) license to any Author to rapidly develop COVID-19 vaccines. Without all their Accepted Manuscript version arising from this submission. efforts, we would not have been able to pursue this work. Data Access: Data used in this paper are openly available We thank the LSST Solar System Science Collaboration for from the Vera C. Rubin Observatory Construction Project and manuscript feedback. The authors thank Mike Brown for useful Operations Teams via https://github.com/lsst-pst/survey_ discussions. We thank the anonymous referee for reading and strategy/tree/main/fbs_1.7 and https://github.com/lsst-pst/ reviewing this very long manuscript and providing constructive survey_strategy/tree/main/fbs_2.0. The rubin_sim/OpSim feedback. The authors also acknowledge the SCOC for their LSST cadence simulation databases are available at https:// service to the Rubin user community. We thank Federica s3df.slac.stanford.edu/data/rubin/sim-data/. Bianco and the American Astronomical Society (AAS) Facility: Rubin. Journals editorial team for facilitating the Rubin LSST Survey Software: LSST Metrics Analysis Framework (MAF, Jones Strategy Optimization ApJS focus issue. et al. 2014), Astropy (Astropy Collaboration et al. This research has made use of NASA’s Astrophysics Data 2013, 2018, 2022), Numpy (van der Walt et al. 2011; Harris System Bibliographic Services. et al. 2020), Matplotlib (Hunter 2007), Pandas (pandas This work was supported in part by the LSSTC Enabling development team, T 2020), rubin_sim/OpSim (Naghib et al. Science grants program, the B612 Foundation, the University of 2019; Jones et al. 2020; Yoachim et al. 2022), sbpy (Mommert Washington’sDiRAC (Data-intensive Research in Astrophysics et al. 2019), JupyterHub (https://jupyterhub.readthedocs.io/ and Cosmology) Institute, the Planetary Society, and Adler en/latest), Jupyter Notebook (Kluyver et al. 2016), Python Planetarium through generous support of the LSST Solar System (https://www.python.org), OpenOrb (Granvik et al. 2009), Readiness Sprints. M.E.S. was supported by the UK Science scipy (Virtanen et al. 2020), healpy (Górski et al. 2005; Zonca Technology Facilities Council (STFC) grant ST/V000691/1, and et al. 2019), seaborn (Waskom 2021). she acknowledges travel support provided by STFC for UK Author Contributions: M.E.S. organized and coordinated the participation in LSST through grant ST/N002512/1. K.V. paper writing effort, as well as the review of the LSST cadence acknowledges support from the Preparing for Astrophysics with LSST Program funded by the Heising Simons Foundation (grant simulations and drafting of formal SSSC feedback to the SCOC 2021–2975),from NSF (grant AST-1824869), and from NASA that this work is derived from. She wrote the abstract, (grants 80NSSC19K0785, 80NSSC21K0376, and 80NSS Sections 1, 2.3.4, 3, 4.1.1, 4.1.2, 4.2.1, 4.4.1, 5.1, and the C22K0512). M.T.B. appreciates support by the Rutherford preambles to Sections 4, 4.1, 4.2, 4.4, 4.7, 5. She also cowrote 58 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Sections 6 and 5.2. She generated the figures showing the Appendix A footprints, discovery, and light-curve metrics, as well as color List of Acronyms light-curve metrics based on software utilities and jupyter A list of acronyms used within this paper is contained in notebooks developed by R.L.J. and P.Y. She also contributed Table 7. to the Planet Nine figures (Figures 6 and 7). She also created Tables 2–6. She also provided feedback on the entire manuscript. R.L.J. and P.Y. provided guidance on the jupyter notebook templates used to develop the paper figures. They Table 7 provided expert feedback on the performance and behavior of List of Acronyms Used in This Paper the Rubin scheduler and metrics. They also contributed to the Acronym Expansion discussions about the MAF metric outputs for all the cadence CFEPS Canada–France Ecliptic Plane Survey simulation families. They also contributed to Figure 24. R.L.J. COSMOS Cosmic Evolution Survey wrote Sections 2.2, 2.3, and subsections within. R.L.J. created CSV comma-separated values Tables 1 and 9 and produced the key plots for Figures 1, 6, and DDF Deep Drilling Fields 7. P.Y. wrote Section 2.1 and generated Figure 39. K.V. wrote DES Dark Energy Survey Sections 4.1.3 and 4.4.2, created Table 7, and provided DESC DC2 Dark Energy Science Collaboration second data challenge feedback on the whole manuscript. R.C.D. wrote Section 4.6 DESI Dark Energy Spectroscopic Instrument and cowrote Sections 5.2 and 5.3. C.O. wrote Sections 4.3 and DIA difference imaging analysis 4.2.2 and provided feedback on the overall manuscript. S.G. ECDFS Extended Chandra Deep Field South EDF-S Euclid Deep Field South aided in reviewing the LSST cadence simulations and drafting ELAISS1 European Large-Area Infrared Space Observatory Survey-S1 the formal SSSC feedback to the SCOC. In particular, she led ETNO extreme trans-Neptunian object the review and formal feedback for the low-SE twilight NEO FOV field of view microsurvey, soliciting and organizing discussion and feedback GP Galactic plane (used in figures) from the NEOs and ISOs SSSC working group. She wrote IEO inner-Earth object Section 4.7.1, cowrote Section 6, and provided feedback on the IOC inner Oort Cloud object overall manuscript. T.L. wrote Sections 4.4.4 (Third Visits in a ISO interstellar object Night) and 4.7.2 (Other Microsurveys), contributed to JFC Jupiter-family comet LC light curve (used in figures) Section 6 (Conclusions), and provided feedback on the overall low-SE low solar elongation manuscript. C.S. wrote Section 4.5 and provided feedback on LSST Legacy Survey of Space and Time the overall manuscript. B.T.B. wrote Section 4.2.3 (Other LSSTCam Legacy Survey of Space and Time Camera Variations of Exposure Times) and provided feedback on MAF Metrics Analysis Framework Sections 4.4.4 (Third Visits in a Night) and 4.7.1 (Low-SE MBA main-belt asteroid Solar System Twilight microsurvey). L.I. wrote Section 4.4.3, MBC main-belt comet contributed to the discussion presented in Section 4.2.2, and MDP Markov decision process provided feedback on the overall manuscript. M.T.B. wrote MMR mean motion resonance MOID minimum orbit intersection distance Section 5.4 and cowrote Section 4.6. S.E. led the writing of MPC Minor Planet Center Section 5.3, provided input on Section 5.4, and contributed the NEO near-Earth object description of the simulated ‘Ayló’chaxnim population in NES Northern Ecliptic Spur Section 2.2. M.S. provided feedback discussion and maintained OCC Oort Cloud comet the list of simulations across the manuscript and figures. M.S. OpSim operations simulation K. wrote the introduction to Section 2, developed the cometary Pan-STARRS Panoramic Survey Telescope and Rapid Response System brightening function implemented in the OCC metric, provided PHA potentially hazardous asteroid input on the OCC simulations, created the orbit OCC files, and PSF point-spread function S3M Synthetic Solar System Model provided feedback on the overall manuscript. M.J. contributed SCOC Survey Cadence Optimization Committee text to Section 4.7.1 and provided feedback on the overall SCP south celestial pole (used in figures) manuscript. H.W.L. created Figure 5. A.T., D.R., M.M.K., R. SED spectral energy distribution M., T.D., and Q.Y. provided feedback on the overall manu- SE solar elongation script. M.G. contributed to the discussions about astrometric SMLV Stars, Milky Way, and Local Volume precision and orbital characterization for Section 2.3.4 and S/N signal-to-noise ratio provided feedback on the overall manuscript. C.L. provided SRD Science Requirements Document feedback on Section 2. P.H.B. and W.J.O. contributed to SSP solar system processing discussions about the Planet Nine discoverability. S.R.C., J.D., SSSC Solar System Science Collaboration TNO trans-Neptunian object D.R., W.C.F., and A.T. contributed to the development of TVS Transients and Variable Stars light-curve metrics. W.C.F. also provided the TNO SED. M.E. WFD Wide–Fast–Deep S., with contributions from R.L.J., M.J., S.G., P.Y., S.E., M.S., XMM-LSS X-ray Multi-Mirror Mission-Newton Large Scale Structure and M.T.B., drafted the response to the referee report and ZTF Zwicky Transient Facility revised the manuscript based on the referee’s feedback. 59 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Appendix B List of LSST Cadence Simulations Table 8 contains a list of the LSST survey strategy simulations used in this work. Table 8 Table of All Simulations Referenced in This Work, Its Family of Simulations, the Baseline Simulation That It Should Be Compared Against, and Which Figures in the Paper Reference the Simulation Simulation Name Family Comparison Baseline Included in Which Figures baseline_nexp1_v1.7_10yrs Baseline N/A 17 baseline_nexp2_v1.7_10yrs Baseline N/A 9, 11, 17, 18, 23, 26 baseline_retrofoot_v2.0_10yrs Baseline N/A 2, 8 baseline_samefilt_v1.5_10yrs intranight baseline_v1.5_10yrs 28, 29 baseline_v1.5_10yrs Baseline N/A 4, 10, 28, 29 baseline_v2.0_10yrs Baseline N/A 2, 8, 12, 13, 14, 19, 27, 31, 32, 33, 34, 37, 38, 43 baseline_v2.1_10yrs Baseline N/A 1, 2, 6, 7, 15, 16, 20, 22, 30 baseline_v2.2_10yrs Baseline N/A 41, 42, 39 bluer_indx0_v2.0_10yrs bluer balance baseline_v2.0_10yrs 19 bluer_indx1_v2.0_10yrs bluer balance baseline_v2.0_10yrs 19 bulges_bs_v1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 bulges_bulge_wfd_v1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 bulges_cadence_bs_v1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 bulges_cadence_bulge_wfd_v1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 bulges_cadence_i_heavy_v1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 bulges_i_heavy_v1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 cadence_drive_gl100_gcbv1.7_10yrs filter_cadence baseline_nexp2_v1.7_10yrs 23 cadence_drive_gl100v1.7_10yrs filter_cadence baseline_nexp2_v1.7_10yrs 23 cadence_drive_gl200_gcbv1.7_10yrs filter_cadence baseline_nexp2_v1.7_10yrs 23 cadence_drive_gl200v1.7_10yrs filter_cadence baseline_nexp2_v1.7_10yrs 23 cadence_drive_gl30_gcbv1.7_10yrs filter_cadence baseline_nexp2_v1.7_10yrs 23 cadence_drive_gl30v1.7_10yrs filter_cadence baseline_nexp2_v1.7_10yrs 23 carina_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 ddf_frac_ddf_per0.6_v2.0_10yrs ddf percent baseline_v2.0_10yrs 38 ddf_frac_ddf_per1.6_v2.0_10yrs ddf percent baseline_v2.0_10yrs 38 filterdist_indx1_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21, filterdist_indx2_v1.5_10yrs Baseline N/A 4, 10, 21 filterdist_indx3_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21 filterdist_indx4_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21 filterdist_indx5_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21 filterdist_indx6_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21 filterdist_indx7_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21 filterdist_indx8_v1.5_10yrs filter_dist filterdist_indx2_v1.5_10yrs 21 footprint_0_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_1_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_2_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_3_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_4_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_5_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_6_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_7_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_8_v1.710yrs footprint_tune baseline_nexp2_v1.7_10yrs 9, 11 footprint_add_mag_cloudsv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_big_sky_dustv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_big_sky_nouiyv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_big_skyv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_big_wfdv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_bluer_footprintv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_gp_smoothv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_newAv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_newBv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 footprint_no_gp_northv1.5_10yrs footprint baseline_v1.5_10yrs 4, 10 good_seeing_gsw0.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_gsw1.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 60 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 8 (Continued) Simulation Name Family Comparison Baseline Included in Which Figures good_seeing_gsw10.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_gsw20.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_gsw3.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_gsw50.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_gsw6.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw0.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw1.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw10.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw20.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw3.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw50.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 good_seeing_u_gsw6.0_v2.1_10yrs good seeing baseline_v2.1_10yrs 22 local_gal_bindx0_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 local_gal_bindx1_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 local_gal_bindx2_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 long_gaps_nightsoff0_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff0_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 31 long_gaps_nightsoff1_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff1_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff2_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff2_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff3_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff3_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff4_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff4_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff5_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff5_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff6_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff6_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff7_delayed-1_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_nightsoff7_delayed1827_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 long_gaps_np_nightsoff0_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff0_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff1_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff1_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff2_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff2_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff3_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff3_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff4_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff4_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff5_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff5_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff6_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff6_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff7_delayed-1_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_gaps_np_nightsoff7_delayed1827_v2.0_10yrs long gaps no pairs baseline_v2.0_10yrs 27 long_u1_v2.0_10yrs longer u visits baseline_v2.0_10yrs 19 long_u2_v2.0_10yrs longer u visits baseline_v2.0_10yrs 19 multi_short_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 no_repeat_rpw-1.0_v2.1_10yrs suppress repeats baseline_v2.1_10yrs 30 no_repeat_rpw-10.0_v2.1_10yrs suppress repeats baseline_v2.1_10yrs 30 no_repeat_rpw-100.0_v2.1_10yrs suppress repeats baseline_v2.1_10yrs 30 no_repeat_rpw-2.0_v2.1_10yrs suppress repeats baseline_v2.1_10yrs 30 no_repeat_rpw-20.0_v2.1_10yrs suppress repeats baseline_v2.1_10yrs 30 no_repeat_rpw-5.0_v2.1_10yrs suppress repeats baseline_v2.1_10yrs 30 noroll_v2.0_10yrs rolling baseline_v2.0_10yrs 33, 37 north_stripe_v2.0_10yrs microsurveys baseline_v2.0_10yrs 2, 43 pair_times_11_v1.7_10yrs pair_times baseline_nexp2_v1.7_10yrs 26 pair_times_22_v1.7_10yrs pair_times baseline_nexp2_v1.7_10yrs 24, 26 pair_times_33_v1.7_10yrs pair_times baseline_nexp2_v1.7_10yrs 24, 26 pair_times_44_v1.7_10yrs pair_times baseline_nexp2_v1.7_10yrs 26 pair_times_55_v1.7_10yrs pair_times baseline_nexp2_v1.7_10yrs 24, 26 61 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 8 (Continued) Simulation Name Family Comparison Baseline Included in Which Figures pencil_fs1_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint pencil_fs2_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 2, 15, 16 footprint plane_priority_priority0.1_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.1_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.2_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.2_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.3_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.3_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.4_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.4_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.6_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.6_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 2, 15, 16 footprint plane_priority_priority0.9_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority0.9_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority1.2_pbf_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint plane_priority_priority1.2_pbt_v2.1_10yrs Galactic plane baseline_v2.1_10yrs 15, 16 footprint presto_gap1.5_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 2, 31, 32 presto_gap1.5_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap2.0_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap2.0_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap2.5_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap2.5_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap3.0_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap3.0_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap3.5_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap3.5_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap4.0_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_gap4.0_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap1.5_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap1.5_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap2.0_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap2.0_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap2.5_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap2.5_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap3.0_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap3.0_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap3.5_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap3.5_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap4.0_mix_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 presto_half_gap4.0_v2.0_10yrs triplets baseline_v2.0_10yrs 31, 32 rolling_all_sky_ns2_rw0.9_v2.0_10yrs rolling baseline_v2.0_10yrs 37 rolling_bulge_6_v2.0_10yrs rolling baseline_v2.0_10yrs 37 rolling_bulge_ns2_rw0.5_v2.0_10yrs rolling baseline_v2.0_10yrs 37 rolling_bulge_ns2_rw0.8_v2.0_10yrs rolling baseline_v2.0_10yrs 37 rolling_bulge_ns2_rw0.9_v2.0_10yrs rolling baseline_v2.0_10yrs 37 rolling_ns2_rw0.5_v2.0_10yrs rolling baseline_v2.0_10yrs 33, 37 rolling_ns2_rw0.9_v2.0_10yrs rolling baseline_v2.0_10yrs 37 62 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 8 (Continued) Simulation Name Family Comparison Baseline Included in Which Figures rolling_ns3_rw0.5_v2.0_10yrs rolling baseline_v2.0_10yrs 33, 37 rolling_ns3_rw0.9_v2.0_10yrs rolling baseline_v2.0_10yrs 35, 37 roman_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 shave_20_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_22_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_25_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_28_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_30_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_32_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_35_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_38_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 shave_40_v2.1_10yrs vary expt baseline_v2.1_10yrs 20 short_exp_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 six_rolling_ns6_rw0.5_v2.0_10yrs rolling baseline_v2.0_10yrs 33, 37 six_rolling_ns6_rw0.9_v2.0_10yrs rolling baseline_v2.0_10yrs 36, 37 smc_movie_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 too_rate10_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 too_rate50_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 twi_neo_brightest_repeat3_iz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_iz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_iz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_iz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_iz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_iz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_iz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_riz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat3_z_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_iz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_riz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_brightest_repeat4_z_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_iz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_iz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_iz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 63 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 8 (Continued) Simulation Name Family Comparison Baseline Included in Which Figures twi_neo_repeat3_iz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_iz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_iz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_iz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_riz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat3_z_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_iz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 40, 41, 42 twi_neo_repeat4_iz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_iz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_iz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_iz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_iz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_iz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_riz_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 40, 41, 42 twi_neo_repeat4_riz_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_riz_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_riz_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_riz_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_riz_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_riz_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np1_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np2_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np3_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np4_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np5_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np6_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 twi_neo_repeat4_z_np7_v2.2_10yrs twilight neo 15s baseline_v2.2_10yrs 41, 42 u_long_ms_30_v1.7_10yrs u_long baseline_nexp2_v1.7_10yrs 18 u_long_ms_40_v1.7_10yrs u_long baseline_nexp2_v1.7_10yrs 18 u_long_ms_50_v1.7_10yrs u_long baseline_nexp2_v1.7_10yrs 18 u_long_ms_60_v1.7_10yrs u_long baseline_nexp2_v1.7_10yrs 18 vary_expt_v2.0_10yrs vary expt baseline_v2.0_10yrs 19 vary_gp_gpfrac0.01_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.05_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.10_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.15_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.20_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.25_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.30_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.35_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.40_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.45_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.50_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.55_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac0.75_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_gp_gpfrac1.00_v2.0_10yrs vary gp baseline_v2.0_10yrs 14 vary_nes_nesfrac0.01_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.05_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.10_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.15_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.20_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 64 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 8 (Continued) Simulation Name Family Comparison Baseline Included in Which Figures vary_nes_nesfrac0.25_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.30_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.35_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.40_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.45_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.50_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.55_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 vary_nes_nesfrac0.75_v2.0_10yrs vary nes baseline_v2.0_10yrs 2, 12, 13 vary_nes_nesfrac1.00_v2.0_10yrs vary nes baseline_v2.0_10yrs 12, 13 virgo_cluster_v2.0_10yrs microsurveys baseline_v2.0_10yrs 43 Note. A detailed summary of each of the simulations and families may be found at https://github.com/lsst-pst/survey_strategy/blob/main/fbs_1.7/SummaryInfo. ipynb (v1.5 and v1.7 simulations) or at https://github.com/lsst-pst/survey_strategy/blob/main/fbs_2.0/SummaryInfo_v2.1.ipynb (v2.0–v2.2 v simulations). (This table is available in machine-readable form.) Appendix C Metrics Values for the v2.1 Baseline Cadence Simulation Metric results for the most recent baseline survey simulation at the time of submission (baseline_v2.1_10yrs) are listed in Table 9. Table 9 Metric Values for baseline_v2.1_10yrs from the Latest Version of rubin_sim Metric Value (%) Completeness PHA H <= 6.0 93.9 Completeness PHA H <= 22.0 59.6 Completeness NEO H <= 16.0 93.0 Completeness NEO H <= 22.0 58.2 Completeness MBA H <= 16.0 100.0 Completeness MBA H <= 21.0 54.3 Completeness Jupiter Trojan H <= 14.0 100.0 Completeness Jupiter Trojan H <= 18.0 43.8 Completeness TNO H <= 6.0 69.9 Completeness TNO H <= 8.0 48.0 Completeness OCC_r5 H <= 8.0 93.8 Completeness OCC_r5 H <= 17.0 64.0 Completeness OCC_r20 H <= 8.0 85.5 Completeness OCC_r20 H <= 12.0 60.5 Completeness ‘Ayló’chaxnim H <= 16.0 0.04 Completeness ‘Ayló’chaxnim H <= 20.5 0.02 Completeness (quads) ‘Ayló’chaxnim H <= 16.0 0.17 Completeness (quads) ‘Ayló’chaxnim H <= 20.5 0.13 Fraction LC Inversion PHA H = 16.0 46.6 Fraction LC Inversion PHA H = 19.0 5.5 Fraction LC Inversion NEO H = 16.0 48.1 Fraction LC Inversion NEO H = 19.0 5.5 Fraction LC Inversion MBA H = 16.0 94.7 Fraction LC Inversion MBA H = 18.0 15.5 Fraction LC Inversion Jupiter Trojan H = 14.0 94.3 Fraction LC Inversion Jupiter Trojan H = 15.0 11.9 Fraction 4 of grizy PHA H = 16.0 84.0 Fraction 4 of grizy PHA H = 19.0 52.3 Fraction 4 of grizy NEO H = 16.0 85.4 Fraction 4 of grizy NEO H = 19.0 52.4 Fraction 4 of grizy MBA H = 16.0 100.0 65 The Astrophysical Journal Supplement Series, 266:22 (68pp), 2023 June Schwamb et al. Table 9 (Continued) Metric Value (%) Fraction 4 of grizy MBA H = 18.0 89.6 Fraction 4 of grizy Jupiter Trojan H = 14.0 100.0 Fraction 4 of grizy Jupiter Trojan H = 15.0 100.0 Fraction 4 filters TNO H = 6.0 59.8 Fraction 4 filters TNO H = 7.0 41.5 Fraction 4 filters OCC_r5 H = 8.0 82.9 Fraction 4 filters OCC_r5 H = 14.0 33.4 Fraction 4 filters OCC_r20 H = 8.0 76.3 Fraction 4 filters OCC_r20 H = 11.0 38.4 Note. These values all represent the percent of the expected population that would “pass” the metric requirements. “Completeness” refers to the discovery completeness for each sample population at the indicated H value, while “Fraction LC Inversion” refers to the fraction of each population that would have observations that meet the metric requirements, implying that the object would be a good subject for light-curve inversion. Likewise for “Fraction 4 filters,” showing the fraction of each population that would be likely to obtain colors in four filters. 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