would fall to LSST to optimize its nightly schedule to maximize the number of near-simultaneous pointings with Euclid. Although an initial assessment of the simultaneous astrometry between LSST and Euclid has been performed and presented at the Rubin Project and Community Workshop in 2019, no simulations currently exist that would quantify the impact of newer LSST cadence simulations with respect to joint Euclid-Rubin solar system science.

## 5.4. Unquantified Considerations: Satellite Constellations

It is now certain that the accelerating industrialization of near-Earth space will have major adverse effects on astronomical observation (Walker et al. 2020a, 2020b, 2021; Hall et al. 2021; Halferty et al. 2022). The future density of global satellite constellations is as yet uncertain, as it depends on commercial and regulatory decisions. However, regulatory approval has been granted by the United States for at least 30,000 satellites (Walker et al. 2021); >2500 of these launched in the past 3 yr, with 212 Starlinks in 2022 May alone.<fnref n="51" /> This means that Rubin Observatory will have to observe into a hyperindustrialized sky. The first few years of LSST will contain the effects of the iterative passes of at least 6000 low-Earth-orbit satellites—and as constellation build-out continues, satellite density will only increase throughout the survey.

For LSSTCam, there are notable streak effects when illuminated satellites cross the focal plane during an LSST exposure (Tyson et al. 2020). The level to which a streak could be partly or fully saturated in the LSST images depends on each satellite's orbit, morphology, reflectance properties, and orientation; the severity can vary through time, such as when a single launch's "train" of co-released satellites are on its orbit raise and are brighter than when on final orbit. In these cases, and also when the satellite is fainter and so the streaks are not saturated, the impacted pixels will not be suitable for photometry: each satellite streak decreases the effective sky coverage of the exposure. Satellites at $m_g \simeq 7$, with brightnesses below saturation though at $S/N \simeq 100$, are also anticipated to create substantial multiorder cross talk. These highly correlated linear "ghost" streaks center on cores surrounded by wings several hundred pixels in extent. The degree to which Rubin Observatory's processing pipelines will be able to mask is yet to be determined. Even if algorithms can be developed for adequate cross-talk removal, spatially correlated noise will still generate systematics throughout the entire LSST data set (Tyson et al. 2020). Additionally, there is an increase in global sky brightness from the ensemble of the size distribution of space debris and satellites—which has already raised the diffuse zenith luminance by 10% as of 2021 (Kocifaj et al. 2021). While the community focus to date has been on the direct solar illumination of Earth-orbiting objects, satellites also reflect moonlight moonshine, and potential additional sources of illumination (e.g., earthshine, mutual reflectance among illuminated space objects) have yet to be modeled in published studies. The industrially caused loss of global darkness will continue as more anthropogenic material is added to Earth's orbital environment. For solar system science, satellites and associated debris will thus have three main effects. They obliterate, or modify in unquantified ways, the photometry of individual object detections that are blazed over by streak footprints; they introduce systematic errors at low

surface brightness into sky that may later be the source of detections when images are stacked; and they reduce the dynamic range available for the lower-S/N solar system detections.

A recent study by Hu et al. (2022) finds that ~10% of all LSST images will have a streak from a launched or planned-to-be-launched Starlink (Generation 1 or 2) or OneWeb satellite, 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 of satellite constellations have not yet been comprehensively quantified for Rubin Observatory's solar system science, and we do not attempt to do so here, as no cadence simulations yet include impacts from these satellite streaks on LSSTCam: we merely highlight a number of potential projected loss effects. First, the shallowing of LSST will decrease the detected solar system populations across any and all cadences. Across all populations, detection loss for individual objects will deplete the sparse light curves LSST generates, for instance, limiting the ability to detect small body activity (Section 2.3.2). Second, there will be population-dependent losses in solar system science from satellite effects. In particular, for detections of NEOs, which individually only become visible for a small subset of time within the span of LSST, the steep size distribution means that the majority of detections are made toward the Rubin magnitude limit. NEO discovery by the SSP pipelines requires a pair of detections (see tracklets) and is thus fragile to satellite effects: losing single detections from a pair has a disproportionate impact on the detectability of this population, as for intranight cadence outcomes (see Sections 4.4.1, 4.4.3, and 4.4.4). Similarly, satellite-generated detection loss will also acutely affect solar system populations that are only visible for week-to-month time periods, such as ISOs and newly active comets. The seasonality of satellite density—more satellites are illuminated, and for longer, in summer—will have a seasonal impact on solar system object detectability (Hainaut & Williams 2020; McDowell 2020; Lawler et al. 2022). Seasonality detection biases adversely affect all solar system populations that cluster on parts of the sky (e.g., Trojan populations, resonant TNOs, potentially the high-q, high-a TNOs, NEOs, and ISOs); they require careful debiasing to generate accurate population models (e.g., Kavelaars et al. 2020). While not quantified for satellites, general outcomes of inducing this type of effect can be considered in the *vary_NES* cadence family (Section 4.1.3). Additionally, twilight-bright satellites will be abundant in the low-SE sky that is being targeted for detection of PHAs, IEOs, gravitationally focused ISOs, and comet comae. Illuminated satellites are most numerous near the horizon close to dawn and dusk; the low-SE twilight microsurvey runs in -12° solar elevation and darker. For the low-SE twilight microsurvey (Section 4.7.1), the effects of satellite constellations will be particularly pronounced, with some 90% of images expected to be impacted with at least one streak per image (Hu et al. 2022).

It may be possible for the Rubin Observatory scheduler to selectively observe specific pointings on the sky, which could decrease the number of WFD images with satellite streaks by a factor of two. However, this would come at the substantive cost of ~10% of the LSST observing time (Hu et al. 2022). The trade-offs of implementing this algorithm would depend on the impact of the satellite streaks on LSSTCam, which, as noted above, has yet to be fully characterized, and the number, sky distribution, and

<footnote n="51">⁵¹ https://www.planet4589.org/space/stats/star/starstats.html</footnote>
apparent magnitude of satellites at the start of Rubin science operations. Overall, the characterization of the impacts of satellite constellations on the LSST cadences, given the ever-changing parameters of constellation design, replenishment, and dynamic operation, will prove challenging. It will require major effort to model and incorporate into future LSST survey simulations.

## 6. Conclusions

By analyzing the LSST cadence simulations and the outputs for a suite of MAF metrics, we have explored the impact on solar system science for a wide range of potential LSST cadences. Our resulting analysis highlights the importance of simulating the expected small body detections for future multipurpose wide-field surveys. This allows for tensions between main science drivers to be identified in order to optimize the on-sky observing and maximize the output from next-generation astronomical surveys and facilities. We hope that this paper and the entire focus issue that this paper contributes to may serve as resources for future SCOC reviews of the LSST cadence, as well as for future wide-field survey design.

In general, we find that a wide range of LSST survey strategies provide satisfactory temporal and spatial coverage to achieve the goals for solar system science outlined in the SSSC Science Roadmap (Schwamb et al. 2018a). Below, we summarize our key findings and recommendations based on the version 1.5–2.2 LSST cadence simulations:

1. Observing the northern regions of the ecliptic up to +10° ecliptic latitude (the NES) is crucial for outer solar system science and probing the solar system small body populations that are asymmetrically distributed on the sky.

2. Covering the NES to at least 25% of the WFD level is critical for discovering and characterizing slowly moving objects (e.g., TNOs) and faint inner solar system objects.

3. Shifting time away from the WFD to the Galactic plane can negatively impact light-curve measurements of faint solar system objects.

4. Shifting the WFD footprint northward from high-extinction regions to low-extinction sky, such that part of the NES region obtains visits at WFD cadence, is a welcome change. The new expanded northern WFD + NES footprint used in the v2.0–v2.2 cadence simulations is conducive to solar system science.

5. We advocate for moving from 2 × 15 s snaps to a single 1 × 30 s exposure per visit owing to the resulting ~8% boost in on-sky visits.

6. Aiming for 33-minute separations between nightly pairs is an ideal compromise between achieving a high pair completion rate per night and for the Rubin SSP pipelines to be sensitive to moving objects at distances up to ~150 au.

7. Shorter exposure times are beneficial for the discovery of PHAs and NEOs, while longer exposures are better for the discovery of more distant objects. Shorter exposures also increase the total number of on-sky visits per pointing, providing denser sampling for light-curve inversion. A compromise between discovery and color/light-curve characterization is to use 30 s exposures per visit when possible.

8. Longer *u*-band exposures (50 s and above) tend to reduce discoveries at small sizes (fainter *H*) and are detrimental to light-curve inversions.

9. Similarly, increasing the number of exposures in bluer filters (*u* and *g*) decreases the number of faint objects for which light-curve inversion is possible.

10. Restricting repeat nightly visits to the same filter does not significantly improve solar system metrics over mixed-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 solar system metrics, particularly if the “Presto-Color” (rapid color; Bianco et al. 2019) strategy is implemented. Adding the third visit in the same color as the earlier pair and increasing the gap from the initial pair is shown to have a much lower impact.

12. Having the Rubin scheduler better balance extra nightly visits beyond pairs in a given night by redistributing them across the sky has some benefit to small body discovery with typically small hits to light-curve characterization.

13. Rolling cadence strategies are generally positive for solar system metrics, although the most extreme rolling patterns (many stripes or very strong rolling) should be avoided. A rolling pattern that ensures a minimum coverage to enable discovery of rare types of objects in the “off” stripes should be considered.

14. Spending 3%–8% of the survey time on DDF observations produces only minimal losses for solar system science. If some DDFs are observed only in certain years, observing the COSMOS DDF for at least 2 yr would be the most beneficial for the detection and orbit characterization of small bodies discovered by shift-and-stack algorithms.

15. The COSMOS DDF should be observed when the Jupiter Trojans are passing through the field, which occurs in discrete windows during LSST.

16. Starting a low-SE twilight microsurvey in Year 1 of operations would make Rubin Observatory uniquely sensitive to several populations of solar system small bodies such as IEOs, Earth Trojans, and sungrazing comets and give the LSST the potential to improve asteroid models, test the theory of asteroid supercatas-trophic disruption at small perihelion distances, and improve asteroid impact warning times. An infrequently run (e.g., every three nights) low-SE twilight microsurvey would also enhance small body discovery and light-curve inversion and enable the discovery of ‘Ayló’chaxnims, which are only visible during twilight.

17. The vast majority of the additional microsurveys for specific regions of sky have little to no effect on the solar system metrics, but there remains the possibility that combining several of these microsurveys could produce a result that causes a large impact later. This will likely need to be the subject of further simulation runs later when the larger and more influential parts of the cadence strategy are decided on and actual operational overheads are measured from commissioning activities.

18. The production of incremental templates in the first year of the LSST is particularly important for the timely follow-up of ISO apparitions and other transient solar system phenomena. Further work is needed to explore the impact on Year 1 discovery rates for the different potential options for creating the incremental templates.

19. A 40 hr program as described in Trilling et al. (2018a) to observe a set of solar system Deep Fields would probe the small size end distribution of the TNO and Neptune
Trojan populations that cannot be achieved with the
currently planned LSST.

20. Creating joint data products with other contemporaneous surveys such as ESA's Euclid would be of great scientific benefit to the solar system science community. Apart from overlapping DDFs, which are already planned, we suggest synchronizing observations of survey fields where possible when choosing LSST nightly cadences.

21. The impact of rapidly increasing industrial activity in near-Earth space is not modeled here, but the projected adverse effects are substantial. We advocate for careful characterization of the anthropogenic impacts on the LSST.

Our analysis has focused on the individual impact of changing
at the same time one or two observing constraints or parameters
within the Rubin scheduler. We have not explored the impact of
changing all these parameters simultaneously. We note that
although tuning individual knobs for the LSST survey strategy by
themselves may have little effect, the combination of several of
them may not. This should be carefully considered by the SCOC
when they finalize their recommendation for the initial LSST
observing cadence. The analysis presented here should be
repeated with the finalized LSST SCOC recommended observing
strategy when it becomes publicly available. Future cadence
simulations should be generated and studied to examine additional
options for the low-SE twilight microsurvey. Further investigation
is also needed to explore how the various options for incremental
template generation will impact the potential for real-time
discovery and follow-up of our solar system's minor planets
and ISOs in the first year of the LSST. This will be particularly
important for assessing whether the low-SE twilight survey should
be included as part of the Year 1 LSST observing strategy.

The authors wish to acknowledge all of the essential workers who have put their health at risk since the start of the COVID-19 global pandemic and the researchers who worked tirelessly to rapidly develop COVID-19 vaccines. Without all their efforts, we would not have been able to pursue this work.

We thank the LSST Solar System Science Collaboration for
manuscript feedback. The authors thank Mike Brown for useful
discussions. We thank the anonymous referee for reading and
reviewing this very long manuscript and providing constructive
feedback. The authors also acknowledge the SCOC for their
service to the Rubin user community. We thank Federica
Bianco and the American Astronomical Society (AAS)
Journals editorial team for facilitating the Rubin LSST Survey
Strategy Optimization ApJS focus issue.

This research has made use of NASA's Astrophysics Data
System Bibliographic Services.

This work was supported in part by the LSSTC Enabling
Science grants program, the B612 Foundation, the University of
Washington's DiRAC (Data-intensive Research in Astrophysics
and Cosmology) Institute, the Planetary Society, and Adler
Planetarium through generous support of the LSST Solar System
Readiness Sprints. M.E.S. was supported by the UK Science
Technology Facilities Council (STFC) grant ST/V000691/1, and
she acknowledges travel support provided by STFC for UK
participation in LSST through grant ST/N002512/1. K.V.
acknowledges support from the Preparing for Astrophysics with
LSST Program funded by the Heising Simons Foundation (grant
2021-2975), from NSF (grant AST-1824869), and from NASA
(grants 80NSSC19K0785, 80NSSC21K0376, and 80NSS
C22K0512). M.T.B. appreciates support by the Rutherford

Discovery Fellowships from New Zealand Government funding, administered by the Royal Society Te Apārangi. M.S.K. was supported by the NASA Solar System Observations program (80NSSC20K0673). H.W.L. is supported by NASA grant NNX17AF21G and by NSF grant AST-2009096. T.D. acknowledges support from the LSSTC Catalyst Fellowship awarded by LSST Corporation with funding from the John Templeton Foundation grant ID No. 62192. S.G. acknowledges support from the DIRAC Institute in the Department of Astronomy at the University of Washington. The DIRAC Institute is supported through generous gifts from the Charles and Lisa Simonyi Fund for Arts and Sciences and the Washington Research Foundation. S.G. also acknowledges support from the Preparing for Astrophysics with LSST Program funded by the Heising Simons Foundation (grant 2021-2975), from NSF (grant OAC-1934752), and from NASA (grant 80NSSC22K0978). The work of S.R.C. was conducted at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. R.C.D. acknowledges support from the UC Doctoral Scholarship and Canterbury Scholarship administered by the University of Canterbury, a PhD research scholarship awarded through M.T.B.'s Rutherford Discovery Fellowship grant, and an LSSTC Enabling Science grant awarded by LSST Corporation. R.M. acknowledges support from NSF (AST-1824869) and NASA (80NSSC19K0785). L.I. acknowledges support from the Italian Space Agency (ASI) within the ASI-INAF agreements I/024/12/0 and 2020-4-HH.0.

This material or work is supported in part by the National
Science Foundation through Cooperative Agreement AST-
1258333 and Cooperative Support Agreement AST1836783
managed by the Association of Universities for Research in
Astronomy (AURA) and the Department of Energy under
contract No. DE-AC02-76SF00515 with the SLAC National
Accelerator Laboratory managed by Stanford University.

For the purpose of open access, the author has applied a
Creative Commons Attribution (CC BY) license to any Author
Accepted Manuscript version arising from this submission.

Data Access: Data used in this paper are openly available
from the Vera C. Rubin Observatory Construction Project and
Operations Teams via https://github.com/lsst-pst/survey-
strategy/tree/main/fbs_1.7 and https://github.com/lsst-pst/
survey_strategy/tree/main/fbs_2.0. The rubin_sim/OpSim
LSST cadence simulation databases are available at https://
s3df.slac.stanford.edu/data/rubin/sim-data/.

Facility: Rubin.

Software: LSST Metrics Analysis Framework (MAF, Jones et al. 2014), Astropy (Astropy Collaboration et al. 2013, 2018, 2022), Numpy (van der Walt et al. 2011; Harris et al. 2020), Matplotlib (Hunter 2007), Pandas (pandas development team, T 2020), rubin_sim/OpSim (Naghib et al. 2019; Jones et al. 2020; Yoachim et al. 2022), sbpy (Mommert et al. 2019), JupyterHub (https://jupyterhub.readthedocs.io/en/latest), Jupyter Notebook (Kluyver et al. 2016), Python (https://www.python.org), OpenOrb (Granvik et al. 2009), scipy (Virtanen et al. 2020), healpy (Górski et al. 2005; Zonca et al. 2019), seaborn (Waskom 2021).

Author Contributions: M.E.S. organized and coordinated the
paper writing effort, as well as the review of the LSST cadence
simulations and drafting of formal SSSC feedback to the SCOC
that this work is derived from. She wrote the abstract,
Sections 1, 2.3.4, 3, 4.1.1, 4.1.2, 4.2.1, 4.4.1, 5.1, and the
preambles to Sections 4, 4.1, 4.2, 4.4, 4.7, 5. She also cowrote
Sections 6 and 5.2. She generated the figures showing the footprints, discovery, and light-curve metrics, as well as color light-curve metrics based on software utilities and jupyter notebooks developed by R.L.J. and P.Y. She also contributed 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 provided expert feedback on the performance and behavior of the Rubin scheduler and metrics. They also contributed to the discussions about the MAF metric outputs for all the cadence simulation families. They also contributed to Figure 24. R.L.J. wrote Sections 2.2, 2.3, and subsections within. R.L.J. created Tables 1 and 9 and produced the key plots for Figures 1, 6, and 7. P.Y. wrote Section 2.1 and generated Figure 39. K.V. wrote Sections 4.1.3 and 4.4.2, created Table 7, and provided feedback on the whole manuscript. R.C.D. wrote Section 4.6 and cowrote Sections 5.2 and 5.3. C.O. wrote Sections 4.3 and 4.2.2 and provided feedback on the overall manuscript. S.G. aided in reviewing the LSST cadence simulations and drafting the formal SSSC feedback to the SCOC. In particular, she led the review and formal feedback for the low-SE twilight NEO microsurvey, soliciting and organizing discussion and feedback from the NEOs and ISOs SSSC working group. She wrote Section 4.7.1, cowrote Section 6, and provided feedback on the overall manuscript. T.L. wrote Sections 4.4.4 (Third Visits in a Night) and 4.7.2 (Other Microsurveys), contributed to Section 6 (Conclusions), and provided feedback on the overall manuscript. C.S. wrote Section 4.5 and provided feedback on the overall manuscript. B.T.B. wrote Section 4.2.3 (Other Variations of Exposure Times) and provided feedback on Sections 4.4.4 (Third Visits in a Night) and 4.7.1 (Low-SE Solar System Twilight microsurvey). L.I. wrote Section 4.4.3, contributed to the discussion presented in Section 4.2.2, and provided feedback on the overall manuscript. M.T.B. wrote Section 5.4 and cowrote Section 4.6. S.E. led the writing of Section 5.3, provided input on Section 5.4, and contributed the description of the simulated ‘Ayló’chaxnim population in Section 2.2. M.S. provided feedback discussion and maintained the list of simulations across the manuscript and figures. M.S. K. wrote the introduction to Section 2, developed the cometary brightening function implemented in the OCC metric, provided input on the OCC simulations, created the orbit OCC files, and provided feedback on the overall manuscript. M.J. contributed text to Section 4.7.1 and provided feedback on the overall manuscript. H.W.L. created Figure 5. A.T., D.R., M.M.K., R. M., T.D., and Q.Y. provided feedback on the overall manuscript. M.G. contributed to the discussions about astrometric precision and orbital characterization for Section 2.3.4 and provided feedback on the overall manuscript. C.L. provided feedback on Section 2. P.H.B. and W.J.O. contributed to discussions about the Planet Nine discoverability. S.R.C., J.D., D.R., W.C.F., and A.T. contributed to the development of light-curve metrics. W.C.F. also provided the TNO SED. M.E. S., with contributions from R.L.J., M.J., S.G., P.Y., S.E., M.S., and M.T.B., drafted the response to the referee report and revised the manuscript based on the referee’s feedback.

## Appendix A
List of Acronyms

A list of acronyms used within this paper is contained in Table 7.

<table><caption>**Table 7**
List of Acronyms Used in This Paper</caption><thead><tr><td>Acronym</td><td>Expansion</td></tr></thead><tbody><tr><td>CFEPS</td><td>Canada-France Ecliptic Plane Survey</td></tr><tr><td>COSMOS</td><td>Cosmic Evolution Survey</td></tr><tr><td>CSV</td><td>comma-separated values</td></tr><tr><td>DDF</td><td>Deep Drilling Fields</td></tr><tr><td>DES</td><td>Dark Energy Survey</td></tr><tr><td>DESC DC2</td><td>Dark Energy Science Collaboration second data challenge</td></tr><tr><td>DESI</td><td>Dark Energy Spectroscopic Instrument</td></tr><tr><td>DIA</td><td>difference imaging analysis</td></tr><tr><td>ECDFS</td><td>Extended Chandra Deep Field South</td></tr><tr><td>EDF-S</td><td>Euclid Deep Field South</td></tr><tr><td>ELAISS1</td><td>European Large-Area Infrared Space Observatory Survey-S1</td></tr><tr><td>ETNO</td><td>extreme trans-Neptunian object</td></tr><tr><td>FOV</td><td>field of view</td></tr><tr><td>GP</td><td>Galactic plane (used in figures)</td></tr><tr><td>IEO</td><td>inner-Earth object</td></tr><tr><td>IOC</td><td>inner Oort Cloud object</td></tr><tr><td>ISO</td><td>interstellar object</td></tr><tr><td>JFC</td><td>Jupiter-family comet</td></tr><tr><td>LC</td><td>light curve (used in figures)</td></tr><tr><td>low-SE</td><td>low solar elongation</td></tr><tr><td>LSST</td><td>Legacy Survey of Space and Time</td></tr><tr><td>LSSTCam</td><td>Legacy Survey of Space and Time Camera</td></tr><tr><td>MAF</td><td>Metrics Analysis Framework</td></tr><tr><td>MBA</td><td>main-belt asteroid</td></tr><tr><td>MBC</td><td>main-belt comet</td></tr><tr><td>MDP</td><td>Markov decision process</td></tr><tr><td>MMR</td><td>mean motion resonance</td></tr><tr><td>MOID</td><td>minimum orbit intersection distance</td></tr><tr><td>MPC</td><td>Minor Planet Center</td></tr><tr><td>NEO</td><td>near-Earth object</td></tr><tr><td>NES</td><td>Northern Ecliptic Spur</td></tr><tr><td>OCC</td><td>Oort Cloud comet</td></tr><tr><td>OpSim</td><td>operations simulation</td></tr><tr><td>Pan-STARRS</td><td>Panoramic Survey Telescope and Rapid Response System</td></tr><tr><td>PHA</td><td>potentially hazardous asteroid</td></tr><tr><td>PSF</td><td>point-spread function</td></tr><tr><td>S3M</td><td>Synthetic Solar System Model</td></tr><tr><td>SCOC</td><td>Survey Cadence Optimization Committee</td></tr><tr><td>SCP</td><td>south celestial pole (used in figures)</td></tr><tr><td>SED</td><td>spectral energy distribution</td></tr><tr><td>SE</td><td>solar elongation</td></tr><tr><td>SMLV</td><td>Stars, Milky Way, and Local Volume</td></tr><tr><td>S/N</td><td>signal-to-noise ratio</td></tr><tr><td>SRD</td><td>Science Requirements Document</td></tr><tr><td>SSP</td><td>solar system processing</td></tr><tr><td>SSSC</td><td>Solar System Science Collaboration</td></tr><tr><td>TNO</td><td>trans-Neptunian object</td></tr><tr><td>TVS</td><td>Transients and Variable Stars</td></tr><tr><td>WFD</td><td>Wide-Fast-Deep</td></tr><tr><td>XMM-LSS</td><td>X-ray Multi-Mirror Mission-Newton Large Scale Structure</td></tr><tr><td>ZTF</td><td>Zwicky Transient Facility</td></tr></tbody></table>
## Appendix B
List of LSST Cadence Simulations

Table 8 contains a list of the LSST survey strategy simulations used in this work.

<table><caption>**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</caption><thead><tr><th>Simulation Name</th><th>Family</th><th>Comparison Baseline</th><th>Included in Which Figures</th></tr></thead><tbody><tr><td>baseline_nexp1_v1.7_10yrs</td><td>Baseline</td><td>N/A</td><td>17</td></tr><tr><td>baseline_nexp2_v1.7_10yrs</td><td>Baseline</td><td>N/A</td><td>9, 11, 17, 18, 23, 26</td></tr><tr><td>baseline_retrofoot_v2.0_10yrs</td><td>Baseline</td><td>N/A</td><td>2, 8</td></tr><tr><td>baselineSameFilt_v1.5_10yrs</td><td>intranight</td><td>baseline_v1.5_10yrs</td><td>28, 29</td></tr><tr><td>baseline_v1.5_10yrs</td><td>Baseline</td><td>N/A</td><td>4, 10, 28, 29</td></tr><tr><td>baseline_v2.0_10yrs</td><td>Baseline</td><td>N/A</td><td>2, 8, 12, 13, 14, 19, 27, 31, 32, 33, 34, 37, 38, 43</td></tr><tr><td>baseline_v2.1_10yrs</td><td>Baseline</td><td>N/A</td><td>1, 2, 6, 7, 15, 16, 20, 22, 30</td></tr><tr><td>baseline_v2.2_10yrs</td><td>Baseline</td><td>N/A</td><td>41, 42, 39</td></tr><tr><td>bluer_index0_v2.0_10yrs</td><td>bluer balance</td><td>baseline_v2.0_10yrs</td><td>19</td></tr><tr><td>bluer_index1_v2.0_10yrs</td><td>bluer balance</td><td>baseline_v2.0_10yrs</td><td>19</td></tr><tr><td>bulges bs_v1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>bulges bulge wfd_v1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>bulges_cadence bs_v1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>bulges_cadence bulge wfd_v1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>bulges_cadence i_heavy_v1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>bulges i_heavy_v1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>cadence drive g1100 gcbv1.7_10yrs</td><td>filter_cadence</td><td>baseline_nexp2_v1.7_10yrs</td><td>23</td></tr><tr><td>cadence drive g1100v1.7_10yrs</td><td>filter_cadence</td><td>baseline_nexp2_v1.7_10yrs</td><td>23</td></tr><tr><td>cadence drive g1200 gcbv1.7_10yrs</td><td>filter_cadence</td><td>baseline_nexp2_v1.7_10yrs</td><td>23</td></tr><tr><td>cadence drive g1200v1.7_10yrs</td><td>filter_cadence</td><td>baseline_nexp2_v1.7_10yrs</td><td>23</td></tr><tr><td>cadence drive g130 gcbv1.7_10yrs</td><td>filter_cadence</td><td>baseline_nexp2_v1.7_10yrs</td><td>23</td></tr><tr><td>cadence drive g130v1.7_10yrs</td><td>filter_cadence</td><td>baseline_nexp2_v1.7_10yrs</td><td>23</td></tr><tr><td>carina_v2.0_10yrs</td><td>microsurveys</td><td>baseline_v2.0_10yrs</td><td>43</td></tr><tr><td>ddffrac ddf_per 0.6_v2.0_10yrs</td><td>ddf percent</td><td>baseline_v2.0_10yrs</td><td>38</td></tr><tr><td>ddffrac ddf_per 1.6_v2.0_10yrs</td><td>ddf percent</td><td>baseline_v2.0_10yrs</td><td>38</td></tr><tr><td>filterdist index1_v1.5_10yrs</td><td>filter_dist</td><td>filterdist index2_v1.5_10yrs</td><td>21</td></tr><tr><td>filterdist index2_v1.5_10yrs</td><td>Baseline</td><td>N/A</td><td>4, 10, 21</td></tr><tr><td>filterdist index3_v1.5_10yrs</td><td>filter_dist</td><td>filterdist index2_v1.5_10yrs</td><td>21</td></tr><tr><td>filterdist index4_v1.5_10yrs</td><td>filter_dist</td><td>filterdist index2_v1.5_10yrs</td><td>21</td></tr><tr><td>filterdist index5_v1.5_10yrs</td><td>filter_dist</td><td>filterdist index2_v1.5_10yrs</td><td>21</td></tr><tr><td>filterdist index6_v1.5_10yrs</td><td>filter_dist</td><td>filterdist index2_v1.5_10yrs</td><td>21</td></tr><tr><td>filterdist index7_v1.5_10yrs</td><td>filter_dist</td><td>filterdist index2_v1.5_10yrs</td><td>21</td></tr><tr><td>filterdist index8_v1.5_10yrs</td><td>filter_dist</td><td>filterdist index2_v1.5_10yrs</td><td>21</td></tr><tr><td>footprint 0_v1.710yrs</td><td>footprint_tune</td><td>baseline_nexp2_v1.7_10yrs</td><td>9, 11</td></tr><tr><td>footprint 1_v1.710yrs</td><td>footprint_tune</td><td>baseline_nexp2_v1.7_10yrs</td><td>9, 11</td></tr><tr><td>footprint 2_v1.710yrs</td><td>footprint_tune</td><td>baseline_nexp2_v1.7_10yrs</td><td>9, 11</td></tr><tr><td>footprint 3_v1.710yrs</td><td>footprint_tune</td><td>baseline_nexp2_v1.7_10yrs</td><td>9, 11</td></tr><tr><td>footprint 4_v1.710yrs</td><td>footprint_tune</td><td>baseline_nexp2_v1.7_10yrs</td><td>9, 11</td></tr><tr><td>footprint 5_v1.710yrs</td><td>footprint_tune</td><td>baseline_nexp2_v1.7_10yrs</td><td>9, 11</td></tr><tr><td>footprint 6_v1.710yrs</td><td>footprint_tune</td><td>baseline_nexp2_v1.7_10yrs</td><td>9, 11</td></tr><tr><td>footprint 7_v1.710yrs</td><td>footprint_tune</td><td>baseline_nexp2_v1.7_10yrs</td><td>9, 11</td></tr><tr><td>footprint 8_v1.710yrs</td><td>footprint_tune</td><td>baseline_nexp2_v1.7_10yrs</td><td>9, 11</td></tr><tr><td>footprint add mag cloudsv1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>footprint big sky dustv1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>footprint big sky nouiyv1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>footprint big skyv1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>footprint big wfdv1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>footprint bluer footprint v1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>footprint gp smooth v1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>footprint newAv1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>footprint newBv1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>footprint no gp north v1.5_10yrs</td><td>footprint</td><td>baseline_v1.5_10yrs</td><td>4, 10</td></tr><tr><td>good seeing gsw 0.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr><tr><td>good seeing gsw 1.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr></tbody></table>
<table><caption>**Table 8**
(Continued)</caption><thead><tr><th>Simulation Name</th><th>Family</th><th>Comparison Baseline</th><th>Included in Which Figures</th></tr></thead><tbody><tr><td>good_seeing_gsw10.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr><tr><td>good_seeing_gsw20.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr><tr><td>good_seeing_gsw3.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr><tr><td>good_seeing_gsw50.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr><tr><td>good_seeing_gsw6.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr><tr><td>good_seeing_u_gsw0.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr><tr><td>good_seeing_u_gsw1.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr><tr><td>good_seeing_u_gsw10.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr><tr><td>good_seeing_u_gsw20.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr><tr><td>good_seeing_u_gsw3.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr><tr><td>good_seeing_u_gsw50.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr><tr><td>good_seeing_u_gsw6.0_v2.1_10yrs</td><td>good seeing</td><td>baseline_v2.1_10yrs</td><td>22</td></tr><tr><td>local_gal_bindx0_v2.0_10yrs</td><td>microsurveys</td><td>baseline_v2.0_10yrs</td><td>43</td></tr><tr><td>local_gal_bindx1_v2.0_10yrs</td><td>microsurveys</td><td>baseline_v2.0_10yrs</td><td>43</td></tr><tr><td>local_gal_bindx2_v2.0_10yrs</td><td>microsurveys</td><td>baseline_v2.0_10yrs</td><td>43</td></tr><tr><td>long_gaps_nightsoff0_delayed-1_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff0_delayed1827_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 31</td></tr><tr><td>long_gaps_nightsoff1_delayed-1_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff1_delayed1827_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff2_delayed-1_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff2_delayed1827_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff3_delayed-1_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff3_delayed1827_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff4_delayed-1_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff4_delayed1827_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff5_delayed-1_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff5_delayed1827_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff6_delayed-1_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff6_delayed1827_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff7_delayed-1_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps_nightsoff7_delayed1827_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>long_gaps NP_nightsoff0_delayed-1_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff0_delayed1827_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff1_delayed-1_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff1_delayed1827_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff2_delayed-1_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff2_delayed1827_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff3_delayed-1_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff3_delayed1827_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff4_delayed-1_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff4_delayed1827_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff5_delayed-1_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff5_delayed1827_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff6_delayed-1_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff6_delayed1827_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff7_delayed-1_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_gaps NP_nightsoff7_delayed1827_v2.0_10yrs</td><td>long gaps no pairs</td><td>baseline_v2.0_10yrs</td><td>27</td></tr><tr><td>long_u1_v2.0_10yrs</td><td>longer u visits</td><td>baseline_v2.0_10yrs</td><td>19</td></tr><tr><td>long_u2_v2.0_10yrs</td><td>longer u visits</td><td>baseline_v2.0_10yrs</td><td>19</td></tr><tr><td>multi_short_v2.0_10yrs</td><td>microsurveys</td><td>baseline_v2.0_10yrs</td><td>43</td></tr><tr><td>no_re repeat_rpw-1.0_v2.1_10yrs</td><td>suppress repeats</td><td>baseline_v2.1_10yrs</td><td>30</td></tr><tr><td>no_re repeat_rpw-10.0_v2.1_10yrs</td><td>suppress repeats</td><td>baseline_v2.1_10yrs</td><td>30</td></tr><tr><td>no_re repeat_rpw-100.0_v2.1_10yrs</td><td>suppress repeats</td><td>baseline_v2.1_10yrs</td><td>30</td></tr><tr><td>no_re repeat_rpw-2.0_v2.1_10yrs</td><td>suppress repeats</td><td>baseline_v2.1_10yrs</td><td>30</td></tr><tr><td>no_re repeat_rpw-20.0_v2.1_10yrs</td><td>suppress repeats</td><td>baseline_v2.1_10yrs</td><td>30</td></tr><tr><td>no_re repeat_rpw-5.0_v2.1_10yrs</td><td>suppress repeats</td><td>baseline_v2.1_10yrs</td><td>30</td></tr><tr><td>noroll_v2.0_10yrs</td><td>rolling</td><td>baseline_v2.0_10yrs</td><td>33, 37</td></tr><tr><td>north_stripe_v2.0_10yrs</td><td>microsurveys</td><td>baseline_v2.0_10yrs</td><td>2, 43</td></tr><tr><td>pair_times_11_v1.7_10yrs</td><td>pair_times</td><td>baseline_nexp2_v1.7_10yrs</td><td>26</td></tr><tr><td>pair_times_22_v1.7_10yrs</td><td>pair_times</td><td>baseline_nexp2_v1.7_10yrs</td><td>24, 26</td></tr><tr><td>pair_times_33_v1.7_10yrs</td><td>pair_times</td><td>baseline_nexp2_v1.7_10yrs</td><td>24, 26</td></tr><tr><td>pair_times_44_v1.7_10yrs</td><td>pair_times</td><td>baseline_nexp2_v1.7_10yrs</td><td>26</td></tr><tr><td>pair_times_55_v1.7_10yrs</td><td>pair_times</td><td>baseline_nexp2_v1.7_10yrs</td><td>24, 26</td></tr></tbody></table>
<table><caption>**Table 8**
(Continued)</caption><thead><tr><th>Simulation Name</th><th>Family</th><th>Comparison Baseline</th><th>Included in Which Figures</th></tr></thead><tbody><tr><td>pencil_fs1_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>pencil_fs2_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>2, 15, 16</td></tr><tr><td>plane_priority_priority0.1_pbf_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>plane_priority_priority0.1_pbt_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>plane_priority_priority0.2_pbf_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>plane_priority_priority0.2_pbt_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>plane_priority_priority0.3_pbf_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>plane_priority_priority0.3_pbt_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>plane_priority_priority0.4_pbf_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>plane_priority_priority0.4_pbt_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>plane_priority_priority0.6_pbf_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>plane_priority_priority0.6_pbt_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>2, 15, 16</td></tr><tr><td>plane_priority_priority0.9_pbf_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>plane_priority_priority0.9_pbt_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>plane_priority_priority1.2_pbf_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>plane_priority_priority1.2_pbt_v2.1_10yrs</td><td>Galactic plane footprint</td><td>baseline_v2.1_10yrs</td><td>15, 16</td></tr><tr><td>presto gap1.5 mix_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>2, 31, 32</td></tr><tr><td>presto gap1.5_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto gap2.0 mix_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto gap2.0_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto gap2.5 mix_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto gap2.5_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto gap3.0 mix_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto gap3.0_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto gap3.5 mix_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto gap3.5_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto gap4.0 mix_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto gap4.0_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto half gap1.5 mix_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto half gap1.5_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto half gap2.0 mix_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto half gap2.0_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto half gap2.5 mix_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto half gap2.5_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto half gap3.0 mix_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto half gap3.0_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto half gap3.5 mix_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto half gap3.5_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto half gap4.0 mix_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>presto half gap4.0_v2.0_10yrs</td><td>triplets</td><td>baseline_v2.0_10yrs</td><td>31, 32</td></tr><tr><td>rolling_all_sky.ns2_rwlock0.9_v2.0_10yrs</td><td>rolling</td><td>baseline_v2.0_10yrs</td><td>37</td></tr><tr><td>rolling bulge_6_v2.0_10yrs</td><td>rolling</td><td>baseline_v2.0_10yrs</td><td>37</td></tr><tr><td>rolling bulge ns2_rwlock0.5_v2.0_10yrs</td><td>rolling</td><td>baseline_v2.0_10yrs</td><td>37</td></tr><tr><td>rolling bulge ns2_rwlock0.8_v2.0_10yrs</td><td>rolling</td><td>baseline_v2.0_10yrs</td><td>37</td></tr><tr><td>rolling bulge ns2_rwlock0.9_v2.0_10yrs</td><td>rolling</td><td>baseline_v2.0_10yrs</td><td>37</td></tr><tr><td>rolling ns2_rwlock0.5_v2.0_10yrs</td><td>rolling</td><td>baseline_v2.0_10yrs</td><td>33, 37</td></tr><tr><td>rolling ns2_rwlock0.9_v2.0_10yrs</td><td>rolling</td><td>baseline_v2.0_10yrs</td><td>37</td></tr></tbody></table>
<table><caption>**Table 8**
(Continued)</caption><thead><tr><th>Simulation Name</th><th>Family</th><th>Comparison Baseline</th><th>Included in Which Figures</th></tr></thead><tbody><tr><td>rolling_ms3_rwlock0.5_v2.0_10yrs</td><td>rolling</td><td>baseline_v2.0_10yrs</td><td>33, 37</td></tr><tr><td>rolling_ms3_rwlock0.9_v2.0_10yrs</td><td>rolling</td><td>baseline_v2.0_10yrs</td><td>35, 37</td></tr><tr><td>roman_v2.0_10yrs</td><td>microsurveys</td><td>baseline_v2.0_10yrs</td><td>43</td></tr><tr><td>shave_20_v2.1_10yrs</td><td>vary expt</td><td>baseline_v2.1_10yrs</td><td>20</td></tr><tr><td>shave_22_v2.1_10yrs</td><td>vary expt</td><td>baseline_v2.1_10yrs</td><td>20</td></tr><tr><td>shave_25_v2.1_10yrs</td><td>vary expt</td><td>baseline_v2.1_10yrs</td><td>20</td></tr><tr><td>shave_28_v2.1_10yrs</td><td>vary expt</td><td>baseline_v2.1_10yrs</td><td>20</td></tr><tr><td>shave_30_v2.1_10yrs</td><td>vary expt</td><td>baseline_v2.1_10yrs</td><td>20</td></tr><tr><td>shave_32_v2.1_10yrs</td><td>vary expt</td><td>baseline_v2.1_10yrs</td><td>20</td></tr><tr><td>shave_35_v2.1_10yrs</td><td>vary expt</td><td>baseline_v2.1_10yrs</td><td>20</td></tr><tr><td>shave_38_v2.1_10yrs</td><td>vary expt</td><td>baseline_v2.1_10yrs</td><td>20</td></tr><tr><td>shave_40_v2.1_10yrs</td><td>vary expt</td><td>baseline_v2.1_10yrs</td><td>20</td></tr><tr><td>short_exp_v2.0_10yrs</td><td>microsurveys</td><td>baseline_v2.0_10yrs</td><td>43</td></tr><tr><td>six_rolling_ms6_rwlock0.5_v2.0_10yrs</td><td>rolling</td><td>baseline_v2.0_10yrs</td><td>33, 37</td></tr><tr><td>six_rolling_ms6_rwlock0.9_v2.0_10yrs</td><td>rolling</td><td>baseline_v2.0_10yrs</td><td>36, 37</td></tr><tr><td>smcMovie_v2.0_10yrs</td><td>microsurveys</td><td>baseline_v2.0_10yrs</td><td>43</td></tr><tr><td>too_rate10_v2.0_10yrs</td><td>microsurveys</td><td>baseline_v2.0_10yrs</td><td>43</td></tr><tr><td>too_rate50_v2.0_10yrs</td><td>microsurveys</td><td>baseline_v2.0_10yrs</td><td>43</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl3_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl4_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl5_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl6_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl7_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl1_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl2_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl3_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl4_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl5_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl6_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl7_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl1_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl2_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl3_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl4_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl5_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl6_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat3_iz_npl7_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl2_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl3_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl4_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl5_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl6_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl7_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl1_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl2_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl3_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl4_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl5_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl6_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl7_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl1_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl2_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl3_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl4_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl5_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl6_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl7_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl1_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl2_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl3_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl4_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl5_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl6_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl7_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl1_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl2_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl3_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl4_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl5_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl6_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl7_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl1_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl2_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl3_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl4_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl5_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl6_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl7_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl1_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl2_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl3_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl4_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl5_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl6_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_brightest_re repeat4_iz_npl7_v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline_v2.2_10yrs</td><td>41, 42</td></tr></tbody></table>
<table><caption>**Table 8**
(Continued)</caption><thead><tr><th>Simulation Name</th><th>Family</th><th>Comparison Baseline</th><th>Included in Which Figures</th></tr></thead><tbody><tr><td>twi_neo_re repeat3_iz NP4 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_iz NP5 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_iz NP6 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_iz NP7 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP1 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP2 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP3 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP4 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP5 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP6 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP7 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP1 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP2 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP3 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP4 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP5 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP6 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat3_ri z NP7 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_iz NP1 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>40, 41, 42</td></tr><tr><td>twi_neo_re repeat4_iz NP2 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_iz NP3 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_iz NP4 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_iz NP5 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_iz NP6 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_iz NP7 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP1 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>40, 41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP2 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP3 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP4 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP5 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP6 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP7 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP1 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP2 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP3 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP4 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP5 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP6 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>twi_neo_re repeat4_ri z NP7 v2.2_10yrs</td><td>twilight neo 15s</td><td>baseline v2.2_10yrs</td><td>41, 42</td></tr><tr><td>u_long_ms_30_v1.7_10yrs</td><td>u_long</td><td>baseline_nexp2_v1.7_10yrs</td><td>18</td></tr><tr><td>u_long_ms_40_v1.7_10yrs</td><td>u_long</td><td>baseline_nexp2_v1.7_10yrs</td><td>18</td></tr><tr><td>u_long_ms_50_v1.7_10yrs</td><td>u_long</td><td>baseline_nexp2_v1.7_10yrs</td><td>18</td></tr><tr><td>u_long_ms_60_v1.7_10yrs</td><td>u_long</td><td>baseline_nexp2_v1.7_10yrs</td><td>18</td></tr><tr><td>vary expt v2.0_10yrs</td><td>vary expt</td><td>baseline v2.0_10yrs</td><td>19</td></tr><tr><td>vary gp gpfrac0.01_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary gp gpfrac0.05_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary gp gpfrac0.10_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary gp gpfrac0.15_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary gp gpfrac0.20_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary gp gpfrac0.25_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary gp gpfrac0.30_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary gp gpfrac0.35_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary gp gpfrac0.40_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary gp gpfrac0.45_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary gp gpfrac0.50_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary gp gpfrac0.55_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary gp gpfrac0.75_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary gp gpfrac1.00_v2.0_10yrs</td><td>vary gp</td><td>baseline v2.0_10yrs</td><td>14</td></tr><tr><td>vary nes nesfrac0.01_v2.0_10yrs</td><td>vary nes</td><td>baseline v2.0_10yrs</td><td>12, 13</td></tr><tr><td>vary nes nesfrac0.05_v2.0_10yrs</td><td>vary nes</td><td>baseline v2.0_10yrs</td><td>12, 13</td></tr><tr><td>vary nes nesfrac0.10_v2.0_10yrs</td><td>vary nes</td><td>baseline v2.0_10yrs</td><td>12, 13</td></tr><tr><td>vary nes nesfrac0.15_v2.0_10yrs</td><td>vary nes</td><td>baseline v2.0_10yrs</td><td>12, 13</td></tr><tr><td>vary nes nesfrac0.20_v2.0_10yrs</td><td>vary nes</td><td>baseline v2.0_10yrs</td><td>12, 13</td></tr></tbody></table>
<table><caption>**Table 8**
(Continued)</caption><thead><tr><th>Simulation Name</th><th>Family</th><th>Comparison Baseline</th><th>Included in Which Figures</th></tr></thead><tbody><tr><td>vary_nes_nesfrac0.25_v2.0_10yrs</td><td>vary nes</td><td>baseline_v2.0_10yrs</td><td>12, 13</td></tr><tr><td>vary_nes_nesfrac0.30_v2.0_10yrs</td><td>vary nes</td><td>baseline_v2.0_10yrs</td><td>12, 13</td></tr><tr><td>vary_nes_nesfrac0.35_v2.0_10yrs</td><td>vary nes</td><td>baseline_v2.0_10yrs</td><td>12, 13</td></tr><tr><td>vary_nes_nesfrac0.40_v2.0_10yrs</td><td>vary nes</td><td>baseline_v2.0_10yrs</td><td>12, 13</td></tr><tr><td>vary_nes_nesfrac0.45_v2.0_10yrs</td><td>vary nes</td><td>baseline_v2.0_10yrs</td><td>12, 13</td></tr><tr><td>vary_nes_nesfrac0.50_v2.0_10yrs</td><td>vary nes</td><td>baseline_v2.0_10yrs</td><td>12, 13</td></tr><tr><td>vary_nes_nesfrac0.55_v2.0_10yrs</td><td>vary nes</td><td>baseline_v2.0_10yrs</td><td>12, 13</td></tr><tr><td>vary_nes_nesfrac0.75_v2.0_10yrs</td><td>vary nes</td><td>baseline_v2.0_10yrs</td><td>2, 12, 13</td></tr><tr><td>vary_nes_nesfrac1.00_v2.0_10yrs</td><td>vary nes</td><td>baseline_v2.0_10yrs</td><td>12, 13</td></tr><tr><td>virgo_cluster_v2.0_10yrs</td><td>microsurveys</td><td>baseline_v2.0_10yrs</td><td>43</td></tr></tbody></table>

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><caption>**Table 9**
Metric Values for baseline_v2.1_10yrs from the Latest Version of rubin_sim</caption><thead><tr><th>Metric</th><th>Value (%)</th></tr></thead><tbody><tr><td>Completeness PHA H &lt;= 6.0</td><td>93.9</td></tr><tr><td>Completeness PHA H &lt;= 22.0</td><td>59.6</td></tr><tr><td>Completeness NEO H &lt;= 16.0</td><td>93.0</td></tr><tr><td>Completeness NEO H &lt;= 22.0</td><td>58.2</td></tr><tr><td>Completeness MBA H &lt;= 16.0</td><td>100.0</td></tr><tr><td>Completeness MBA H &lt;= 21.0</td><td>54.3</td></tr><tr><td>Completeness Jupiter Trojan H &lt;= 14.0</td><td>100.0</td></tr><tr><td>Completeness Jupiter Trojan H &lt;= 18.0</td><td>43.8</td></tr><tr><td>Completeness TNO H &lt;= 6.0</td><td>69.9</td></tr><tr><td>Completeness TNO H &lt;= 8.0</td><td>48.0</td></tr><tr><td>Completeness OCC_r5 H &lt;= 8.0</td><td>93.8</td></tr><tr><td>Completeness OCC_r5 H &lt;= 17.0</td><td>64.0</td></tr><tr><td>Completeness OCC_r20 H &lt;= 8.0</td><td>85.5</td></tr><tr><td>Completeness OCC_r20 H &lt;= 12.0</td><td>60.5</td></tr><tr><td>Completeness 'Ayló'chaxnim H &lt;= 16.0</td><td>0.04</td></tr><tr><td>Completeness 'Ayló'chaxnim H &lt;= 20.5</td><td>0.02</td></tr><tr><td>Completeness (quads) 'Ayló'chaxnim H &lt;= 16.0</td><td>0.17</td></tr><tr><td>Completeness (quads) 'Ayló'chaxnim H &lt;= 20.5</td><td>0.13</td></tr><tr><td>Fraction LC Inversion PHA H = 16.0</td><td>46.6</td></tr><tr><td>Fraction LC Inversion PHA H = 19.0</td><td>5.5</td></tr><tr><td>Fraction LC Inversion NEO H = 16.0</td><td>48.1</td></tr><tr><td>Fraction LC Inversion NEO H = 19.0</td><td>5.5</td></tr><tr><td>Fraction LC Inversion MBA H = 16.0</td><td>94.7</td></tr><tr><td>Fraction LC Inversion MBA H = 18.0</td><td>15.5</td></tr><tr><td>Fraction LC Inversion Jupiter Trojan H = 14.0</td><td>94.3</td></tr><tr><td>Fraction LC Inversion Jupiter Trojan H = 15.0</td><td>11.9</td></tr><tr><td>Fraction 4 of grizy PHA H = 16.0</td><td>84.0</td></tr><tr><td>Fraction 4 of grizy PHA H = 19.0</td><td>52.3</td></tr><tr><td>Fraction 4 of grizy NEO H = 16.0</td><td>85.4</td></tr><tr><td>Fraction 4 of grizy NEO H = 19.0</td><td>52.4</td></tr><tr><td>Fraction 4 of grizy MBA H = 16.0</td><td>100.0</td></tr></tbody></table>
<table><caption>**Table 9**
(Continued)</caption><thead><tr><th>Metric</th><th>Value (%)</th></tr></thead><tbody><tr><td>Fraction 4 of grizy MBA H = 18.0</td><td>89.6</td></tr><tr><td>Fraction 4 of grizy Jupiter Trojan H = 14.0</td><td>100.0</td></tr><tr><td>Fraction 4 of grizy Jupiter Trojan H = 15.0</td><td>100.0</td></tr><tr><td>Fraction 4 filters TNO H = 6.0</td><td>59.8</td></tr><tr><td>Fraction 4 filters TNO H = 7.0</td><td>41.5</td></tr><tr><td>Fraction 4 filters OCC_r5 H = 8.0</td><td>82.9</td></tr><tr><td>Fraction 4 filters OCC_r5 H = 14.0</td><td>33.4</td></tr><tr><td>Fraction 4 filters OCC_r20 H = 8.0</td><td>76.3</td></tr><tr><td>Fraction 4 filters OCC_r20 H = 11.0</td><td>38.4</td></tr></tbody></table>

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. Full descriptions of the metrics are listed in Tables 2 and 3. Values for other cadence simulations can be found at https://github.com/lsst-pst/survey_strategy/tree/main/fbs_1.7 and https://github.com/lsst-pst/survey_strategy/tree/main/fbs_2.0.

## ORCID iDs

Megan E. Schwamb https://orcid.org/0000-0003-4365-1455

R. Lynne Jones https://orcid.org/0000-0001-5916-0031

Peter Yoachim https://orcid.org/0000-0003-2874-6464

Kathryn Volk https://orcid.org/0000-0001-8736-236X

Rosemary C. Dorsey https://orcid.org/0000-0002-8910-1021

Cyrielle Opitom https://orcid.org/0000-0002-9298-7484

Sarah Greenstreet https://orcid.org/0000-0002-4439-1539

Tim Lister https://orcid.org/0000-0002-3818-7769

Colin Snodgrass https://orcid.org/0000-0001-9328-2905

Bryce T. Bolin https://orcid.org/0000-0002-4950-6323

Laura Inno https://orcid.org/0000-0002-0271-2664

Michele T. Bannister https://orcid.org/0000-0003-3257-4490

Siegfried Eggl https://orcid.org/0000-0002-1398-6302

Michael Solontoi https://orcid.org/0000-0002-1701-8974

Michael S. P. Kelley https://orcid.org/0000-0002-6702-7676

Mario Jurić https://orcid.org/0000-0003-1996-9252

Hsing Wen Lin

(林省文) https://orcid.org/0000-0001-7737-6784

Darin Ragozzine https://orcid.org/0000-0003-1080-9770

Pedro H. Bernardinelli https://orcid.org/0000-0003-0743-9422

Steven R. Chesley https://orcid.org/0000-0003-3240-6497

Tansu Daylan https://orcid.org/0000-0002-6939-9211

Josef Durech https://orcid.org/0000-0003-4914-3646

Wesley C. Fraser https://orcid.org/0000-0001-6680-6558

Mikael Granvik https://orcid.org/0000-0002-5624-1888

Matthew M. Knight https://orcid.org/0000-0003-2781-6897

Carey M. Lisse https://orcid.org/0000-0002-9548-1526

Renu Malhotra https://orcid.org/0000-0002-1226-3305

William J. Oldroyd https://orcid.org/0000-0001-5750-4953

Audrey Thirouin https://orcid.org/0000-0002-1506-4248

Quanzhi Ye (叶泉志) https://orcid.org/0000-0002-4838-7676

Adams, E. R., Gulbis, A. A. S., Elliot, J. L., et al. 2014, AJ, 148, 55

A'Hearn, M. F., Schleicher, D. G., Millis, R. L., Feldman, P. D., & Thompson, D. T. 1984, AJ, 89, 579

Amiaux, J., Scaramella, R., Mellier, Y., et al. 2012, Proc. SPIE, 8442, 84420Z

Andreoni, I., Coughlin, M. W., Almuaalla, M., et al. 2022, ApJS, 258, 5

Astropy Collaboration, Price-Whelan, A. M., Lian Lim, P., et al. 2022, ApJ, 936, 167

Astropy Collaboration, Price-Whelan, A. M., Sipőcz, B. M., et al. 2018, AJ, 156, 123

Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33

Bannister, M. T., Gladman, B. J., Kavelaars, J. J., et al. 2018, ApJS, 236, 18

Batygin, K., Adams, F. C., Brown, M. E., & Becker, J. C. 2019, PhR, 805, 1

Batygin, K., & Brown, M. E. 2016, AJ, 151, 22

Bell, K. J., & Hermes, J. J. 2018, arXiv:1812.03143

Bellm, E. C., Burke, C. J., Coughlin, M. W., et al. 2022, ApJS, 258, 13

Belyakov, M., Bernardinelli, P. H., & Brown, M. E. 2022, AJ, 163, 216

Bernardinelli, P. H., Bernstein, G. M., Sako, M., et al. 2020, PSJ, 1, 28

Bernardinelli, P. H., Bernstein, G. M., Sako, M., et al. 2022, ApJS, 258, 41

Bianco, F. & the SCOC 2022, Survey Cadence Optimization Committee's Phase 2 Recommendations, Cadence Note PSTN-055, https://pstn-055.lsst.io/

Bianco, F. B., Drout, M. R., Graham, M. L., et al. 2019, PASP, 131, 068002

Bianco, F. B., Ivezić, Ž., Jones, R. L., et al. 2022, ApJS, 258, 1

Bolin, B., Jedicke, R., Granvik, M., et al. 2014, Icar, 241, 280

Bolin, B. T., Ahumada, T., van Dokkum, P., et al. 2022, MNRAS, 517, L49

Bolin, B. T., Fremling, C., Holt, T. R., et al. 2020a, ApJL, 900, L45

Bolin, B. T., Lisse, C. M., Kasliwal, M. M., et al. 2020b, AJ, 160, 26

Bolin, B. T., Masci, F. J., Ye, Q. Z., et al. 2020c, Minor Planet Electronic Circular, MPEC 2020-A99, https://minorplanetcenter.net/mpec/K20/K20A99.html

Bolin, B. T. B., Ahumada, T., van Dokkum, P., et al. 2023, Icar, 394, 115442

Borisov, G. 2019, Minor Planet Electronic Circular, MPEC 2019-R106, https://www.minorplanetcenter.net/mpec/K19/K19RA6.html

Bosch, J., Lupton, R., & Slater, C. 2019, Crowded Field Photometry in LSST Data Release Production, Cadence Note DMTN-129, https://dmtn-129.lsst.io/

Brandt, W. N., Ni, Q., Yang, G., et al. 2018, arXiv:1811.06542

Brasser, R., Duncan, M. J., & Levison, H. F. 2006, Icar, 184, 59

Brasser, R., Duncan, M. J., Levison, H. F., Schwamb, M. E., & Brown, M. E. 2012, Icar, 217, 1

Brasser, R., Higuchi, A., & Kaib, N. 2010, A&A, 516, A72

Brown, M. 2022, Planet Nine reference population, Version 2.0, v2.0, CaltechDATA, doi:10.22002/D1.11545

Brown, M. E., & Batygin, K. 2016, ApJL, 824, L23

Brown, M. E., & Batygin, K. 2019, AJ, 157, 62

Brown, M. E., & Batygin, K. 2021, AJ, 162, 219

Brown, M. E., & Batygin, K. 2022, AJ, 163, 102

Capak, P., Cuillandre, J.-C., Bernardeau, F., et al. 2019a, arXiv:1904.10439

Capak, P., Sconlic, D., Cuillandre, J.-C., et al. 2019b, arXiv:1904.10438

Carry, B. 2018, A&A, 609, A113

Chang, C.-K., Chen, Y.-T., Fraser, W. C., et al. 2021, PSJ, 2, 191

## References

Abareshi, B., Aguilar, J., Ahlen, S., et al. 2022, AJ, 164, 207
Chang, C.-K., Ip, W.-H., Lin, H.-W., et al. 2014, ApJ, 788, 17

Chang, C.-K., Lin, H.-W., Ip, W.-H., et al. 2019, ApJS, 241, 6

Chang, C.-K., Yeh, T.-S., Tan, H., et al. 2022, ApJL, 932, L5

Connolly, A. J., Angeli, G. Z., Chandrasekharan, S., et al. 2014, Proc. SPIE, 9150, 915014

Cook, N. V., Ragozzine, D., Granvik, M., & Stephens, D. C. 2016, ApJ, 825, 51

de la Fuente Marcos, C., & de la Fuente Marcos, R. 2020a, MNRAS, 494, L6

de la Fuente Marcos, C., & de la Fuente Marcos, R. 2020b, MNRAS, 494, 1089

de la Fuente Marcos, C., & de la Fuente Marcos, R. 2022, RNAAS, 6, 160

Delgado, F., & Reuter, M. A. 2016, Proc. SPIE, 9910, 991013

Delgado, F., Saha, A., Chandrasekharan, S., et al. 2014, Proc. SPIE, 9150, 915015

DeMeo, F. E., Binzel, R. P., Slivan, S. M., & Bus, S. J. 2009, Icar, 202, 160

Dones, L., Brasser, R., Kaib, N., & Rickman, H. 2015, SSRv, 197, 191

Đurech, J., Hanuš, J., Oszkiewicz, D., & Vančo, R. 2016, A&A, 587, A48

Eggł, S., Jones, L., & Jurić, M. 2019, LSST Asteroid Discovery Rates, Cadence Note DMTN-109, https://dmtn-109.lsst.io/

Emel'yanenko, V. V. 2022, A&A, 662, L4

Engelhardt, T., Jedicke, R., Vereš, P., et al. 2017, AJ, 153, 133

Everhart, E. 1967, AJ, 72, 716

Fedorets, G., Granvik, M., Jones, R. L., Jurić, M., & Jedicke, R. 2020a, Icar, 338, 113517

Fedorets, G., Micheli, M., Jedicke, R., et al. 2020b, AJ, 160, 277

Ferguson, H. C., Banerji, M., Brough, S., et al. 2021, The LSST Galaxies Science Collaboration Response to the Survey Cadence Optimization Committee Call, Cadence Note, https://docushare.lsst.org/docushare/dsweb/Get/Document-37637/Galaxies_Summary.pdf

Fernández, J. A. 1997, Icar, 129, 106

Francis, P. J. 2005, ApJ, 635, 1348

Gehrels, T., & Jedicke, R. 1996, EM&P, 72, 233

Gladman, B., & Chan, C. 2006, ApJ, 643, L135

Gladman, B., Lawler, S. M., Petit, J. M., et al. 2012, AJ, 144, 23

Gladman, B., & Volk, K. 2021, ARA&A, 59, 203

Górski, K. M., Hivon, E., Banday, A. J., et al. 2005, ApJ, 622, 759

Granvik, M., Morbidelli, A., Jedicke, R., et al. 2016, Natur, 530, 303

Granvik, M., Morbidelli, A., Jedicke, R., et al. 2018, Icar, 312, 181

Granvik, M., Vaubailon, J., & Jedicke, R. 2012, Icar, 218, 262

Granvik, M., Virtanen, J., Oszkiewicz, D., & Muinonen, K. 2009, M&PS, 44, 1853

Grav, T., Jedicke, R., Denneau, L., et al. 2011, PASP, 123, 423

Grav, T., Mainzer, A. K., & Spahr, T. 2016, AJ, 151, 172

Greenstreet, S. 2020, MNRAS, 493, L129

Greenstreet, S., Ngo, H., & Gladman, B. 2012, Icar, 217, 355

Guy, L. P., Bellm, E., Blum, B., et al. 2021, Rubin Observatory Plans for an Early Science Program, Cadence Note RTN-011, https://rtn-011.lsst.io/

Guy, L. P., Cuillandre, J.-C., Bachelet, E., et al. 2022, arXiv:2201.03862

Hainaut, O. R., & Williams, A. P. 2020, A&A, 636, A121

Halferty, G., Reddy, V., Campbell, T., Battle, A., & Furfaro, R. 2022, MNRAS, 516, 1502

Hall, J., Walker, C., Rawls, M., et al. 2021, BAAS, 2, 205

Hanuš, J. 2018, Icar, 299, 84

Hanuš, J., Đurech, J., Brož, M., et al. 2011, A&A, 530, A134

Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Natur, 585, 357

Hergenrother, C. W., & Whiteley, R. J. 2011, Icar, 214, 194

Higuchi, A. 2020, AJ, 160, 134

Higuchi, A., Kokubo, E., Kinoshita, H., & Mukai, T. 2007, AJ, 134, 1693

Holwerda, B. W., Baker, A., Blyth, S., et al. 2018, arXiv:1812.03144

Hoover, D. J., Seligman, D. Z., & Payne, M. J. 2022, PSJ, 3, 71

Hu, J. A., Rawls, M. L., Yoachim, P., & Ivezić, Ž. 2022, ApJL, 941, L15

Huang, Y., Gladman, B., Beaudoin, M., & Zhang, K. 2022, ApJL, 938, L23

Hunter, J. D. 2007, CSE, 9, 90

Ip, W.-H., Masci, F. J., Ye, Q., et al. 2022, ApJL, 935, L6

Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111

Ivezić, Ž., Tabachnik, S., Rafikov, R., et al. 2001, AJ, 122, 2749

Ivezić, Ž. & the LSST Science Collaboration 2013, LSST Science Requirements Document, Cadence Note LPM-17, http://ls.st/LPM-17

Ivezić, Ž. & the SCOC 2021, Survey Cadence Optimization Committee's Phase 1 Recommendation, Cadence Note PSTN-053, https://pstn-053.lsst.io/

Jedicke, R., Bolin, B., Granvik, M., & Beshore, E. 2016, Icar, 266, 173

Jedicke, R., Bolin, B. T., Bottke, W. F., et al. 2018, FrASS, 5, 13

Jones, R. L., Chesley, S. R., Connolly, A. J., et al. 2009, EM&P, 105, 101

Jones, R. L., Slater, C. T., Moeyens, J., et al. 2018, Icar, 303, 181

Jones, R. L., Yoachim, P., Chandrasekharan, S., et al. 2014, Proc. SPIE, 9149, 91490B

Jones, R. L., Yoachim, P., Ivezic, Z., Neilsen, E. H., & Ribeiro, T. 2020, Survey Strategy and Cadence Choices for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), v1.2, Zenodo, doi:10.5281/zenodo.4048838

Jurić, M., Axelrod, T., Becker, A., et al. 2021, Data Products Definition Document, Cadence Note LSE-163, https://lse-163.lsst.io/

Jurić, M., Eggl, S., Moeyens, J., & Jones, L. 2020, Proposed Modifications to Solar System Processing and Data Products, Cadence Note DMTN-087, https://dmtn-087.lsst.io/

Kaib, N. A., Roškar, R., & Quinn, T. 2011, Icar, 215, 491

Kavelaars, J. J., Lawler, S. M., Bannister, M. T., & Shankman, C. 2020, in The Trans-Neptunian Solar System, ed. D. Prialnik, M. A. Barucci, & L. Young (Amsterdam: Elsevier), 61

Kluyver, T., Ragan-Kelley, B., Pérez, F., et al. 2016, in Positioning and Power in Academic Publishing: Players, Agents and Agendas, ed. F. Loizides & B. Schmid (Amsterdam: IOS Press), 87

Kocifaj, M., Kundracik, F., Barentine, J. C., & Bará, S. 2021, MNRAS, 504, L40

Kwiątkowski, T., Kryszczyńska, A., Polińska, M., et al. 2009, A&A, 495, 967

Larson, S., Beshore, E., Hill, R., et al. 2003, AAS/DPS Meeting, 35, 36.04

Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, arXiv:1110.3193

Lawler, S. M., Bohley, A. C., & Rein, H. 2022, AJ, 163, 21

Levine, W. G., Cabot, S. H. C., Seligman, D., & Laughlin, G. 2021, ApJ, 922, 39

Li, X., Ragosta, F., Clarkson, W. I., & Bianco, F. B. 2022, ApJS, 258, 2

Lin, H. W., Chen, Y.-T., Volk, K., et al. 2021, Icar, 361, 114391

Lin, H. W., Gerdes, D. W., Hamilton, S. J., et al. 2019, Icar, 321, 426

Lochner, M., Scolnic, D., Almoubayyed, H., et al. 2022, ApJS, 259, 58

Lochner, M., Scolnic, D. M., Awan, H., et al. 2018, arXiv:1812.00515

LSST Dark Energy Science Collaboration (LSST DESC), Abolfathi, B., Alonso, D., et al. 2021, ApJS, 253, 31

LSST Science Collaboration, Abell, P. A., Allison, J., et al. 2009, arXiv:0912.0201

LSST Science Collaboration, Marshall, P., Anguita, T., et al. 2017, arXiv:1708.04058

Luu, J. X., & Jewitt, D. 1988, AJ, 95, 1256

Malhotra, R. 2019, NatAs, 3, 193

Markwardt, L., Gerdes, D. W., Malhotra, R., et al. 2020, MNRAS, 492, 6105

Marzari, F., Scholl, H., Murray, C., & Lagerkvist, C. 2002, in Asteroids III, ed. W. F. Bottke (Tucson, AZ: Univ. Arizona Press), 725

Masiero, J., Jedicke, R., Đurech, J., et al. 2009, Icar, 204, 145

McCully, C., Crawford, S., Kovacs, G., et al. 2018, Astrophy/Astroscrappy: v1.0.5 Zenodo Release, v1.0.5, Zenodo, doi:10.5281/zenodo.1482019

McDowell, J. C. 2020, ApJL, 892, L36

Meech, K. J., Weryk, R., Micheli, M., et al. 2017, Natur, 552, 378

Mommert, M., Kelley, M., de Val-Borro, M., et al. 2019, JOSS, 4, 1426

Morbidelli, A., & Levison, H. F. 2004, AJ, 128, 2564

Moreno, F., Pozuelos, F. J., Novaković, B., et al. 2017, ApJL, 837, L3

Moro-Martín, A., Turner, E. L., & Loeb, A. 2009, ApJ, 704, 733

Muinonen, K., Torppa, J., Wang, X. B., Cellino, A., & Penttilä, A. 2020, A&A, 642, A138

Myers, J., Jones, L., & Axelrod, T. 2013, Moving Object Pipeline System Design, Cadence Note LDM-156, https://docushare.lsst.org/docushare/dsweb/Get/LDM-156/LDM-156.pdf

Naghib, E., Yoachim, P., Vanderbei, R. J., Connolly, A. J., & Jones, R. L. 2019, AJ, 157, 151

Naidu, S. P., Micheli, M., Farnocchia, D., et al. 2021, ApJL, 913, L6

Napier, K. J., Gerdes, D. W., Lin, H. W., et al. 2021, PSJ, 2, 59

Oldroyd, W. J., & Trujillo, C. A. 2021, AJ, 162, 39

Olsen, K., Di Criscienzo, S., Jones, R. L., et al. 2018, arXiv:1812.02204

pandas development team, T 2020, pandas-dev/pandas: Pandas, v2.0.1, Zenodo, doi:10.5281/zenodo.3509134

Petit, J. M., Kavelaars, J. J., Gladman, B. J., et al. 2011, AJ, 142, 131

Popescu, M., de Leo, J., de la Fuente Marcos, C., et al. 2020, MNRAS, 496, 3572

Pravec, P., & Harris, A. W. 2000, Icar, 148, 12

Raymond, S. N., & Nesvorný, D. 2022, Vesta and Ceres: Insights from the Dawn Mission for the Origin of the Solar System (Cambridge: Cambridge Univ. Press), 227

Rhoads, J. E. 2000, PASP, 112, 703

Schwamb, M. E., Jones, R. L., Chesley, S. R., et al. 2018a, arXiv:1802.01783

Schwamb, M. E., Jurić, M., Bolin, B. T., et al. 2021, RNAAS, 5, 143

Schwamb, M. E., Volk, K., Wen, H., et al. 2018b, arXiv:1812.01149

Scolnic, D. M., Lochner, M., Gris, P., et al. 2018, arXiv:1812.00516

Seaman, R., Abell, P., Christensen, E., et al. 2018, arXiv:1812.00466

Seligman, D., & Laughlin, G. 2018, AJ, 155, 217
Setzer, C. N., Biswas, R., Peiris, H. V., et al. 2019, MNRAS, 485, 4260

Shamir, L. 2005, AN, 326, 428

Shankman, C., Kavelaars, J. J., Bannister, M. T., et al. 2017, AJ, 154, 50

Shannon, A., Jackson, A. P., Veras, D., & Wyatt, M. 2015, MNRAS, 446, 2059

Shao, M., Nemati, B., Zhai, C., et al. 2014, ApJ, 782, 1

Sheppard, S. S., Tholen, D., Pokorny, P., et al. 2022, AJ, 164, 168

Sheppard, S. S., & Trujillo, C. 2016, AJ, 152, 221

Shober, P. M., Jansen-Sturgeon, T., Sansom, E. K., et al. 2019, AJ, 158, 183

Silsbee, K., & Tremaine, S. 2016, AJ, 152, 103

Slyusarev, I. G., & Belskaya, I. N. 2014, SoSyR, 48, 139

Smotherman, H., Connolly, A. J., Kalmbach, J. B., et al. 2021, AJ, 162, 245

Snodgrass, C., Carry, B., Berthier, J., et al. 2018, arXiv:1812.00607

Snodgrass, C., & Jones, G. H. 2019, NatCo, 10, 5418

Solontoi, M., Ivezić, Ž., West, A. A., et al. 2010, Icar, 205, 605

Tonry, J. L., Denneau, L., Heinze, A. N., et al. 2018, PASP, 130, 064505

Trilling, D. E., Bannister, M., Fuentes, C., et al. 2018a, arXiv:1812.09705

Trilling, D. E., Bellm, E. C., & Malhotra, R. 2018b, AJ, 155, 243

Trilling, D. E., Robinson, T., Roegge, A., et al. 2017, ApJL, 850, L38

Trujillo, C. A., & Brown, M. E. 2001, ApJL, 554, L95

Trujillo, C. A., & Sheppard, S. S. 2014, Natur, 507, 471

Tyson, J. A., Ivezić, Ž., Bradshaw, A., et al. 2020, AJ, 160, 226

van der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, CSE, 13, 22

van Dokkum, P. G. 2001, PASP, 113, 1420

Vereš, P., & Chesley, S. R. 2017, AJ, 154, 13

Verma, A., Collett, T., Smith, G. P. & Strong Lensing Science Collaboration 2019, arXiv:1902.05141

Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, NatMe, 17, 261

Vokrouhlický, D., Nesvorný, D., & Dones, L. 2019, AJ, 157, 181

Volk, K., Murray-Clay, R., Gladman, B., et al. 2016, AJ, 152, 23

Walker, C., Di Pippo, S., Aubé, M., et al. 2020a, Dark and Quiet Skies for Science and Society: Report and recommendations, Zenodo, doi:10.5281/zenodo.5898785

Walker, C., Di Pippo, S., Aubé, M., et al. 2021, Dark and Quiet Skies II Working Group Reports, v1, Zenodo, doi:10.5281/zenodo.5874725

Walker, C., Hall, J., Allen, L., et al. 2020b, BAAS, 52, 0206

Warner, B. D., Harris, A. W., & Pravec, P. 2009, Icar, 202, 134

Warner, B. D., Harris, A. W., & Pravec, P. 2021, Asteroid Lightcurve Database (LCDB) Bundle v4.0, NASA Planetary Data System doi:10.26033/j3xc-3359

Waskom, M. 2021, JOSS, 6, 3021

Ye, Q., Kelley, M. S. P., Bolin, B. T., et al. 2020, AJ, 159, 77

Ye, Q., Masci, F. J., Lin, H. W., et al. 2019, PASP, 131, 078002

Yoachim, P. 2021, Survey Strategy: Rolling Cadence, Cadence Note PSTN-052, https://pstn-052.lsst.io/

Yoachim, P. 2022, Survey Strategy Simulation v2.x Results, Cadence Note SMTN-017, https://smtn-017.lsst.io/

Yoachim, P., Coughlin, M., Angeli, G. Z., et al. 2016, Proc. SPIE, 9910, 99101A

Yoachim, P., Jones, L., Eric, H. N. J., et al. 2022, lsst/rubin_sim: Version 1.0, v1.0.0, Zenodo, doi:10.5281/zenodo.7374619

Zderic, A., & Madigan, A.-M. 2020, AJ, 160, 50

Zonca, A., Singer, L., Lenz, D., et al. 2019, JOSS, 4, 1298