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REVIEW 4 major objections 5 minor 1 cited by

Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science: Incremental Templates in Year 1

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper predicts that incremental template generation in LSST Year 1 will delay real-time solar system discoveries by 2-3 months and reduce main-belt asteroid discovery metrics by up to 63% (79% in the North Ecliptic Spur).

desk verdict First explicit Year 1 LSST incremental-template simulation with a robust qualitative result; the headline percentages rest on an unvalidated template-quality assumption. read the letter →

arxiv 2411.19796 v2 pith:NASGQEXQ submitted 2024-11-29 astro-ph.EP astro-ph.IM

James E. Robinson , Megan E. Schwamb , R. Lynne Jones , Mario Jurić , Peter Yoachim , Bryce T. Bolin , Colin O. Chandler , Steven R. Chesley
show 8 more authors
Grigori Fedorets Wesley C. Fraser Sarah Greenstreet Henry H. Hsieh Lauren J. McGinley Stephanie R. Merritt Cyrielle Opitom John K. Parejko
This is my paper · ORCID
classification astro-ph.EPastro-ph.IM
keywords LSSTobservingstrategyincrementaltemplatesdifferenceimagingsolarsystemobjectsmain-beltasteroidsNorthEclipticSpursurveysimulation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper asks what will happen to real-time asteroid and comet discoveries during the first year of the Rubin Observatory's LSST, when the static-sky templates needed for difference imaging have to be built from images taken as the survey proceeds. Simulating the planned single-exposure cadence and generating templates every 3, 7, 14, or 28 days from four acceptable images per patch, the authors predict that solar system discoveries will not appear in the real-time alert stream for roughly the first two to three months of the survey. Across the whole sky the discovery metric for main-belt asteroids falls by up to 63% relative to a baseline that assumes templates already exist, and by up to 79% for main-belt asteroids in the North Ecliptic Spur, the region scheduled for the fewest visits. The losses are worst for faint objects and for the u and g filters, and a monthly template cadence performs worst. Because the template strategy has not yet been finalized, the result matters directly for planning Year 1 operations and for expectations about time-critical follow-up of objects such as potentially hazardous asteroids and interstellar objects.

What carries the argument

The engine of the argument is the incremental template: a static-sky image built from at least four previous exposures in the same filter that pass loose quality cuts (seeing within a factor of two of the best available, depth within 0.5 magnitudes), regenerated on a fixed timescale of 3, 7, 14, or 28 days. Templates are evaluated at the patch scale—small sky tiles comparable to one detector—and a visit counts as usable for real-time discovery only if at least 90% of its area has a template at the time of exposure. The discovery metric then checks whether a simulated solar system object would produce three pairs of detections within 15 nights, the criterion for a new small-body discovery. This chain—four images, then a patch template, then 90% visit coverage, then three nightly pairs—carries the entire result and turns cadence statistics into a predicted drop in discovery completeness.

What would settle it

If the actual LSST alert stream begins reporting solar system discoveries within the first month of science operations, or if Year 1 main-belt discovery completeness relative to a template-ready baseline drops far less than the predicted 28-63%, the central claim would be falsified.

Watch

Extended reading notes

Core claim

The central claim is that an incremental template strategy built from regular LSST observations will severely reduce real-time solar system discovery in Year 1, even when templates are rebuilt every three days. Difference imaging—subtracting a stored image of the static sky from a fresh exposure to reveal moving or variable sources—requires a template, so any patch without one is blind to nightly moving-object detection. The paper predicts a roughly 50-day ramp-up before significant template coverage accumulates, followed by about two weeks for the required sequence of detections, so solar system discoveries begin about 2-3 months after survey start. Measured with the survey's standard discovery criterion (three pairs of detections within 15 nights), template generation lowers completeness by at least 28% for every population tested, with main-belt asteroids losing more than 40% for brighter objects and more than 60% for fainter ones; restricted to the North Ecliptic Spur, faint main-belt discoveries fall by up to 79%. The same mechanism hits filters and regions with few scheduled visits hardest: by the end of Year 1 the u and g filters reach only 20-42% of their baseline cumulative area, and the North Ecliptic Spur dominates the overall loss. The paper's recommendation follows directly: generate templates on weekly or shorter timescales and consider reallocating Year 1 visits toward the North Ecliptic Spur and the u and g filters.

Load-bearing premise

The result rests on the untested assumption that a template built from the first four images passing the paper's broad seeing and depth cuts is good enough for difference imaging and moving-object detection; real sky validation with the commissioning camera has not yet been done.

Editorial extensions

If this is right

  • Real-time LSST solar system alerts will effectively be silent for the first 2-3 months of Year 1, since roughly 50 days are needed to accumulate enough images for templates and another two weeks to satisfy the three-nightly-pair discovery criterion.
  • Year 1 main-belt asteroid discovery completeness falls by 28-63% depending on object brightness and template cadence, with the North Ecliptic Spur alone losing up to 79% of faint main-belt discoveries.
  • Generating templates every 3-7 days noticeably outperforms a monthly cadence, and all tested metrics are best at the shortest timescales.
  • The u and g filters lag farthest behind, reaching only 20-42% of baseline cumulative sky area by the end of Year 1, which also hampers transient science that pairs bluer filters with r in nightly observations.
  • The lost detections are not permanently destroyed, since annual data releases reprocess all Year 1 images, but the real-time alerts and rapid follow-up of rare objects such as interstellar objects, potentially hazardous asteroids, and mini-moons are what the survey gives up under this template strategy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the four-image template proves too shallow or artifact-ridden when tested on sky, the real delay will exceed 2-3 months, because additional re-observations would be needed before a usable template exists.
  • The same template-supply logic applies to every Year 1 alert-based transient science case, not just solar system objects, so supernova and other transient discovery rates will face the same filter- and region-dependent shortfalls.
  • A scheduler change that reallocates a modest number of visits to the North Ecliptic Spur and to the u and g filters in Year 1 is a direct, testable remedy: the paper's own metrics could quantify the recovered completeness in a follow-up simulation without changing the 10-year survey totals.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper uses the Metric Analysis Framework (MAF) and the public one_snap_v4.0_10yrs Rubin cadence simulation to model incremental template generation during Year 1 of LSST. For template-generation intervals of 3, 7, 14, and 28 days, it treats a healpixel as having a template once at least four prior visits in the same filter pass broad per-healpixel seeing and depth cuts, and then retains for solar-system discovery metrics only those visits whose footprint has at least 90% of healpixels templated. The resulting Year 1 visit set is compared with the baseline simulation in which templates are implicitly assumed to exist, using the Schwamb et al. (2023) MAF discovery metrics for MBAs, NEOs, PHAs, TNOs, and OCCs. The central quantitative predictions are that SSO discoveries begin roughly 2–3 months after survey start, that template coverage lags most strongly in u/g bands and in the North Ecliptic Spur, and that MBA discovery metrics decrease by up to 63% over the whole sky and up to 79% in the NES region.

Significance. If the quantitative predictions hold, this is an operationally important result for Rubin Observatory planning: it quantifies the Year 1 real-time solar-system discovery cost of incremental templates and gives the SCOC and operations teams a concrete argument for prioritizing short template-generation cadences and NES/u/g coverage. The paper's strengths are its transparent simulation chain, use of publicly available rubin_sim and MAF inputs rather than fitted parameters, the explicit exploration of four template cadences, and the public release of derived template-coverage databases. The weakness is that the headline numbers rest on an unvalidated template-quality equivalence: four images passing broad per-healpixel cuts are treated as producing templates equivalent to the full-depth templates implicitly assumed in the baseline MAF metrics. Because the authors themselves state that on-sky tests and further investigation are needed, the significance is real but conditional; the manuscript needs a sensitivity analysis around that premise before the headline numbers can be taken at face value.

major comments (4)
  1. [Sections 2.5.2, 2.6, and 3.3] The central quantitative claims (the 2–3 month delay and the 63%/79% metric drops) depend on the assumption that a template constructed from four images that merely pass the broad relative cuts in Section 2.5.1 is equivalent, for SSO detection, to the full-quality templates implicitly assumed in the baseline MAF metrics. The manuscript itself acknowledges in Section 3.1 that 'further investigation into the quality of observations required for suitable incremental templates is needed' and in Section 4 that first-four-image templates 'may have more artifacts and/or lower SNR,' yet no sensitivity analysis bounds this premise. I request a sensitivity test varying the minimum number of template images (e.g., 2, 3, and 5) and, ideally, a model of reduced detection efficiency or increased false-positive rate for four-image templates. If four images are insufficient, the real degradation is worse than reported; if two or three suffice, the delay and metric drops shrink. As written, the headline numbers are conditional on an unvalidated equivalence.
  2. [Section 2.6] The statement 'We do not adjust the 5-σ limiting magnitude of an LSST observation based on the properties of its associated healpixels' templates' is load-bearing for the faint-object results in Figure 21 (right panel). Because difference-image detection is limited by template depth and SNR, counting every templated visit at full baseline detection efficiency will overestimate faint SSO discoveries whenever templates are built from early, relatively shallow images. The paper should estimate the distribution of template depth relative to the science-visit depth for the four-image templates and rerun the faint-object metrics under a conservative depth penalty, or provide a quantitative argument that the effect is negligible for the populations and H bins used here.
  3. [Section 3.3 and Figure 22; Abstract] The abstract attributes the 79% drop to 'MBAs in the NES alone,' but the analysis in Section 3.3 does not isolate the NES footprint: it splits the survey into Dec ≥ 0 and Dec < 0, using the northern-declination half as a proxy for the NES. The Dec ≥ 0 sample includes substantial non-NES sky, including parts of the WFD, so the abstract's wording overstates the geographic specificity of the result. Please recompute the discovery metrics using the actual NES region mask (e.g., the footprint labels in Figure 1) and report those numbers, or rephrase the abstract and Section 3.3 to say 'northern-declination sky' instead of 'NES alone.'
  4. [Abstract, Section 3.3, and Figures 21–22] The abstract states that the whole-sky MBA discovery metric decreases by 'up to 63%' and the NES/Dec≥0 metric by 'up to 79%,' but these exact numbers are not directly traceable to the plotted results. Section 3.3 reports a range of '28–63%' and the summary bullet reports '>40%' and '>60%' for MBA drops, while Figure 21 shows relative values around 0.4–0.6 for the MBA populations. Please provide the numerical values behind the 63% and 79% claims in the text (with the population, H bin, and Δt to which they correspond), or adjust the abstract to match the figures. The headline numbers need to be reproducible from the presented results.
minor comments (5)
  1. [Abstract and Section 2.1] The simulation name appears as 'one_snap_v4.0_10yrs})' in the abstract with a stray closing brace; please fix the typo and use one consistent monospaced notation throughout.
  2. [Section 2.6] The description of template generation says 'If at least four images fitting these criteria are overlapping the healpixel, then the template for that healpixel is assumed to have been generated at tn−1,' but it is not stated whether, when more than four qualifying images exist, the template is built from all of them or from the first four; Figure 8 suggests the number varies, so a sentence clarifying this would help reproducibility.
  3. [Section 3.2, Table 5] The estimate of the number of visits used for template generation is derived by multiplying first-template healpixel counts by healpixel area and dividing by camera footprint area; because healpixels and patch footprints are not aligned, this number is approximate, and the text should state the expected systematic uncertainty from this approximation rather than giving the values as exact counts.
  4. [Section 3.3, Figure 23] The claim in the text that discoveries are delayed by 'approximately 70 days' is based on the cumulative-completeness curves, but the definition of 'delay' (e.g., time to reach a fixed completeness fraction versus horizontal shift of the curves) is not specified; please state the operational definition used to compute the 70-day value.
  5. [Section 4] The recommendation to explore weekly or shorter template cadences is well supported by the analysis, but the text should also note explicitly that the gains from Δt = 3 vs 7 days are small (a few percent in the discovery metrics), so the operational cost of nightly or 3-day processing may not be justified by solar-system discovery alone.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the headline predictions follow from a public cadence simulation and an independently defined MAF metric, while the template-quality premise is an acknowledged assumption rather than a fitted input.

full rationale

The paper's derivation chain is self-contained rather than circular. The inputs are the public one_snap_v4.0_10yrs rubin_sim cadence simulation, the MAF framework, and the Schwamb et al. (2023) solar-system discovery metrics; none of these are fitted to the paper's conclusions. The 'predictions' (2-3 month delay; 63%/79% MBA metric drops) are computed, not assumed: Section 2.6 counts healpixels meeting the four-image quality threshold at each template-generation epoch, Section 2.7 removes visits with less than 90% template coverage, and Section 3.3 runs the pre-existing MAF discovery metrics on the redacted database relative to the baseline. The only place where an input resembles an output is the four-image and 90%-coverage thresholds, but these are stated assumptions in Sections 2.5.2 and 2.7, not quantities derived from the target conclusion; the paper explicitly flags that on-sky validation is still needed ('further investigation into the quality of observations required for suitable incremental templates is needed') and that first-four-image templates 'may have more artifacts and/or lower SNR'. The Schwamb et al. (2023) metric is a self-citation by overlapping authors, but it is used as an external benchmark, is implemented in the open-source MAF, and is not adjusted to produce the reported drops, so it does not constitute circularity. Therefore, no circular step meets the quoting-and-reduction standard, and the appropriate score is 0.

Assumptions & free parameters 5 free parameters · 8 assumptions · 0 invented entities

The headline predictions come from a simulation whose parameters, four template images, the seeing ratio cut, the depth tolerance, the 90% visit coverage gate, and the cadence grid, are chosen by the authors rather than from finalized Rubin requirements. No free parameter is fitted to observed data, so the model is not circular, but all quantitative outputs are conditional on these choices. No new physical entities are introduced.

free parameters (5)
  • minimum template images = 4
    Section 2.5.2; the authors add one image to the 3 proposed in Graham et al. 2020 and Guy et al. 2023. No on-sky validation is available.
  • template seeing quality ratio = < 2
    Section 2.5.1; broad threshold chosen to exclude the worst-seeing observations; not varied in a sensitivity test.
  • template depth tolerance = 0.5 mag
    Section 2.5.1; chosen to reject the shallowest images relative to the best available depth; not varied.
  • visit template coverage threshold = 90%
    Section 2.7; visits with less than 90% template coverage are removed before running SSO discovery metrics. Justified by the bimodal coverage distribution but not optimized.
  • template generation cadence = 3, 7, 14, 28 days
    Section 2.4; scenario grid chosen to bracket plausible production schedules. The reported discovery losses depend on this grid, with 3 and 7 days performing best and 28 days worst.
assumptions (8)
  • domain assumption Without a pre-existing template, the RPP and SSP pipelines cannot difference-image a visit, so no real-time moving-object alerts are produced.
    Sections 1 and 2.7; this is the core pipeline design assumption equating missing templates with lost nightly discoveries.
  • ad hoc to paper A template built from four or more suitable images is sufficient for difference imaging.
    Section 2.5.2; the authors choose four images based on DM technical notes and DP0.2/DC2 coverage, but the requirement is not finalized and not sensitivity-tested.
  • ad hoc to paper Image quality cuts (seeing ratio below 2 and depth within 0.5 mag of the best available) define suitable template images.
    Section 2.5.1; broad thresholds chosen by the authors; changing them changes template availability and downstream metrics.
  • domain assumption SSO discovery requires 3 nightly pairs of detections within 15 nights, as implemented in the MAF metric.
    Table 3 and Section 2.7; this is the adopted SSP detection criterion from Schwamb et al. 2023.
  • ad hoc to paper A visit is only useful for SSO discovery when at least 90% of its healpixels have templates.
    Section 2.7; the 90% gate is a modeling choice justified by the bimodal distribution in Figure 19, but it is not externally established.
  • domain assumption The rubin_sim one_snap_v4.0_10yrs pointing history accurately represents Year 1 operations, including weather, downtime, and scheduler behavior.
    Section 2.1; all results inherit the fidelity of this simulation, which is itself an approximation of the real survey.
  • domain assumption No templates are available from commissioning observations in Year 1.
    Section 4; the authors explicitly assume no significant commissioning contribution. If real commissioning templates exist, the delay and losses shrink.
  • domain assumption HEALPix at nside=256 approximates Rubin patch-level template coverage accurately enough.
    Section 2.3; healpixels of about 13.7 arcmin are comparable to patches but not aligned with them; the authors state that overlap errors balance out on average.

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Cite this review

Pith. "Pith review of Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science: Incremental Templates in Year 1." pith.science (2026). https://pith.science/paper/NASGQEXQ

@misc{pith2026241119796,
  author       = {Pith},
  title        = {Pith review of: Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science: Incremental Templates in Year 1},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NASGQEXQ}},
  note         = {Machine review of arXiv:2411.19796}
}
abstract

The Vera C. Rubin Observatory is due to commence the 10-year Legacy Survey of Space and Time (LSST) at the end of 2025. To detect transient/variable sources and identify solar system objects (SSOs), the processing pipelines require templates of the static sky to perform difference imaging. During the first year of the LSST, templates must be generated as the survey progresses, otherwise SSOs cannot be discovered nightly. The incremental template generation strategy has not been finalized; therefore, we use the Metric Analysis Framework (MAF) and a simulation of the survey cadence (one_snap_v4.0_10yrs}) to explore template generation in Year 1. We have assessed the effects of generating templates over timescales of days-weeks, when at least four images of sufficient quality are available for $\geq90\%$ of the visit. We predict that SSO discoveries will begin $\sim$2-3 months after the start of the survey. We find that the ability of the LSST to discover SSOs in real-time is reduced in Year 1. This is especially true for detections in areas of the sky that receive fewer visits, such as the North Ecliptic Spur (NES), and in less commonly used filters, such as the $u$ and $g$-bands. The lack of templates in the NES dominates the loss of real-time SSO discoveries; across the whole sky the MAF Main-Belt asteroid (MBA) discovery metric decreases by up to $63\%$ compared to the baseline observing strategy, whereas the metric decreases by up to $79\%$ for MBAs in the NES alone.

Figures

Figures reproduced from arXiv: 2411.19796 by the authors.

Figure 1
Figure 1. Left: A sky map showing the total number of visits, in all filters, at the end of Year 1 for the one snap v4.0 10yrs observing strategy (Mollweide projection). This sky map was generated using a HEALPix (Hierarchical Equal Area isoLatitude Pixelization; G´orski et al. 2005) resolution of nside = 256. The plots are centered on right ascension α = 0 and declination δ = 0 degrees, with RA increasing to the left. RA and… view at source ↗
Figure 2
Figure 2. Sky maps of the total number of visits per filter accumulated during Year 1 of the one snap v4.0 cadence simulation. Visits associated with DDFs and the low-solar elongation twilight survey have been removed. In each panel, we display the number of visits in each filter (ugrizy) to highlight the variations in footprint area and coverage (note the different ranges of each color bar). The sky maps were generated using… view at source ↗
Figure 3
Figure 3. A snapshot from an animation showing the Year 1 sky coverage over time for the one snap v4.0 observing strategy. The animation steps through the first year of the simulated LSST in intervals of 7 days, displaying the cumulative number of on-sky visits in the r filter. The plots are centered on right ascension = 0 and declination = 0 degrees. RA and Dec lines are marked every 30◦ . (An animation of this figure is ava… view at source ↗
Figures from the paper (44 more)
Figure 4
Figure 4. Figure 4: A cartoon schematic of tracts and patches in the LSST on-sky data management tessellation. The sky is divided into overlapping tracts each 1.6 ◦ × 1.6 ◦ . Each tract is comprised of 49 overlapping patches. Patches are roughly the size of an individual LSSTCam CCD detec…
Figure 5
Figure 5. Figure 5: A representative patch randomly selected from the Rubin DP 0.2/DESC DC2 (LSST Dark Energy Science Col￾laboration (LSST DESC) et al. 2021) simulated data products Year 1 annual templates. The extent of a single patch is 13.7 × 13.7 arcmin. The colorbar reflects are how …
Figure 6
Figure 6. Figure 6: Sky maps showing the total number of visits with templates, per healpixel (nside = 256), at the end of Year 1, assuming a template generation timescale of 7 days. Each panel shows the results for the ugrizy filters. Compare to [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Same as [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Histogram plots showing the quality statistics of the images incorporated into template images across the sky (as a fraction of unique footprint area in a given filter) for various generation timescales. Results are shown here for the r filter. The upper panel shows th…
Figure 9
Figure 9. Figure 9: Sky maps (nside = 256) showing the difference between total number of visits with templates at the end of Year 1 and the nominal one snap v4.0 survey, for the u, g and r filters. Left: Template generation timescale ∆t = 7d. Right: Template generation timescale ∆t = 28d…
Figure 10
Figure 10. Figure 10: Histograms showing the fractional sky area (y axis) for which healpixels have 0, 1, 2... visits (x axis) at the end of Year 1 considering all filters. We show the histogram distribution for the baseline where template generation is assumed (blue). The distributions wh…
Figure 11
Figure 11. Figure 11: Per filter histograms showing the fraction of sky area with a given number of visits per healpixel (similar to [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: Sky maps for template generation timescale ∆t = 7d showing the number of visits at the end of Year 1 in different regions of the sky for the g (on the left) and r (on the right) filters. Sample positions representative of the NES and WFD (RA, Dec positions of (90, 20)…
Figure 13
Figure 13. Figure 13: The panels show a zoomed-in cutout view (gnomic projection) of the sky map at the indicated locations in [PITH_FULL_IMAGE:figures/full_fig_p019_13.png]
Figure 14
Figure 14. Figure 14: Same as [PITH_FULL_IMAGE:figures/full_fig_p020_14.png]
Figure 15
Figure 15. Figure 15: The cumulative healpixel area with templates for a given filter in Year 1 of LSST. Each panel shows the results for the ugrizy filters, respectively. The area covered by the nominal one snap v4.0 survey (assuming all templates exist at all times) is shown by the black…
Figure 16
Figure 16. Figure 16: An alternative to [PITH_FULL_IMAGE:figures/full_fig_p022_16.png]
Figure 17
Figure 17. Figure 17: Plots showing the cumulative fraction of the Year 1 survey footprint area (i.e. the unique on-sky area covered by that filter in the Year 1 survey) against deltaNight, which is the number of days between the first visit to a healpixel and the night at which a template…
Figure 18
Figure 18. Figure 18: Histogram of the fractional template coverage for all Year 1 visits, which is determined from the number of healpixels within the visit footprint with templates. Results are shown for the one snap v4.0 cadence simulation, assuming a range of template generation timesc…
Figure 19
Figure 19. Figure 19: 2-Dimensional histogram distributions showing how the fractional template coverage of all visits changes as a function of survey time. The logarithmic color scale indicates the number of visits with a particular template coverage on a given date. Results are shown for…
Figure 20
Figure 20. Figure 20: A snapshot of a video animation of the sky coverage over time in one snap v4.0 observing strategy in the case where templates are produced incrementally every ∆t = 7 days over the first year of the simulated survey. The animation steps through the first year of the si…
Figure 21
Figure 21. Figure 21: Here we show the results of running the MAF SSO discovery metrics on a redacted Year 1 visit database of visits with template coverage ≥ 90%. This metric requires 3 detection pairs over the space of 15 nights. We consider several dynamical populations which are indica…
Figure 22
Figure 22. Figure 22: These panels show the same discovery metrics as [PITH_FULL_IMAGE:figures/full_fig_p027_22.png]
Figure 23
Figure 23. Figure 23: Plots showing the discovery completeness of each population during Year 1 of the survey, i.e. the cumulative fraction of objects that have been discovered as a function of time. The line colors distinguish the one snap v4.0 survey where template generation has been im…
Figure 24
Figure 24. Figure 24: Histogram plots showing the quality statistics of the images incorporated into template images across the sky (as a fraction of unique footprint area in a given filter) for various generation timescales. Results are shown here for the u filter. The upper panel shows t…
Figure 25
Figure 25. Figure 25: Same as [PITH_FULL_IMAGE:figures/full_fig_p033_25.png]
Figure 26
Figure 26. Figure 26: Same as [PITH_FULL_IMAGE:figures/full_fig_p034_26.png]
Figure 27
Figure 27. Figure 27: Same as [PITH_FULL_IMAGE:figures/full_fig_p035_27.png]
Figure 28
Figure 28. Figure 28: Same as [PITH_FULL_IMAGE:figures/full_fig_p036_28.png]
Figure 29
Figure 29. Figure 29: Same as [PITH_FULL_IMAGE:figures/full_fig_p037_29.png]
Figure 30
Figure 30. Figure 30: Sky maps (nside = 256) showing the difference between total number of visits with templates at the end of Year 1 and the nominal one snap v4.0 survey, for the u, g and r filters. Left: Template generation timescale ∆t = 7d. Right: Template generation timescale ∆t = 28…
Figure 31
Figure 31. Figure 31: Same as [PITH_FULL_IMAGE:figures/full_fig_p039_31.png]
Figure 32
Figure 32. Figure 32: Per filter histograms showing the fraction of sky area with a given number of visits per healpixel, comparing the baseline to a template generation timescale of ∆t = 3 d [PITH_FULL_IMAGE:figures/full_fig_p040_32.png]
Figure 33
Figure 33. Figure 33: Same as [PITH_FULL_IMAGE:figures/full_fig_p041_33.png]
Figure 34
Figure 34. Figure 34: Same as [PITH_FULL_IMAGE:figures/full_fig_p042_34.png]
Figure 35
Figure 35. Figure 35: Same as [PITH_FULL_IMAGE:figures/full_fig_p043_35.png]
Figure 36
Figure 36. Figure 36: Same as [PITH_FULL_IMAGE:figures/full_fig_p044_36.png]
Figure 37
Figure 37. Figure 37: Same as [PITH_FULL_IMAGE:figures/full_fig_p045_37.png]
Figure 38
Figure 38. Figure 38: Same as [PITH_FULL_IMAGE:figures/full_fig_p046_38.png]
Figure 39
Figure 39. Figure 39: Same as [PITH_FULL_IMAGE:figures/full_fig_p047_39.png]
Figure 40
Figure 40. Figure 40: Same as [PITH_FULL_IMAGE:figures/full_fig_p048_40.png]
Figure 41
Figure 41. Figure 41: Same as [PITH_FULL_IMAGE:figures/full_fig_p049_41.png]
Figure 42
Figure 42. Figure 42: Same as [PITH_FULL_IMAGE:figures/full_fig_p050_42.png]
Figure 43
Figure 43. Figure 43: Same as [PITH_FULL_IMAGE:figures/full_fig_p051_43.png]
Figure 44
Figure 44. Figure 44: Same as [PITH_FULL_IMAGE:figures/full_fig_p052_44.png]
Figure 45
Figure 45. Figure 45: Same as [PITH_FULL_IMAGE:figures/full_fig_p053_45.png]
Figure 46
Figure 46. Figure 46: Same as [PITH_FULL_IMAGE:figures/full_fig_p054_46.png]
Figure 47
Figure 47. Figure 47: Same as [PITH_FULL_IMAGE:figures/full_fig_p055_47.png]

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  1. [1]

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  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ur...

  3. [3]

    3 3)x@|a&:&+^ &NnbК2 TĂ,

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command E...

    work page arXiv 2021

  4. [4]

    W., Almualla , M., et al

    Andreoni , I., Coughlin , M. W., Almualla , M., et al. 2022, , 258, 5, 10.3847/1538-4365/ac3bae

    doi:10.3847/1538-4365/ac3bae 2022

  5. [5]

    2020, Plans and Policies for LSST Alert Distribution

    Bellm, E., Blum, R., Graham, M., et al. 2020, Plans and Policies for LSST Alert Distribution . https://ldm-612.lsst.io/

    work page 2020

  6. [6]

    2022, Survey Cadence Optimization Committee's Phase 2 Recommendations

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

    work page 2022

  7. [7]

    2024, Survey Cadence Optimization Committee's Phase 3 Recommendations

    ---. 2024, Survey Cadence Optimization Committee's Phase 3 Recommendations. https://pstn-056.lsst.io/

    work page 2024

  8. [8]

    B., Drout , M

    Bianco , F. B., Drout , M. R., Graham , M. L., et al. 2019, , 131, 068002, 10.1088/1538-3873/ab121a

    doi:10.1088/1538-3873/ab121a 2019

Show all 56 references
  1. [9]

    B., Ivezi \'c , Z ., Jones , R

    Bianco , F. B., Ivezi \'c , Z ., Jones , R. L., et al. 2022, , 258, 1, 10.3847/1538-4365/ac3e72

    2022 doi

  2. [10]

    T., Fremling, C., Holt, T

    Bolin, B. T., Fremling, C., Holt, T. R., et al. 2020, The Astrophysical Journal Letters, 900, L45, 10.3847/2041-8213/abae69

    2020 doi

  3. [11]

    T., Ahumada, T., van Dokkum , P., et al

    Bolin, B. T., Ahumada, T., van Dokkum , P., et al. 2022, Monthly Notices of the Royal Astronomical Society: Letters, slac089, 10.1093/mnrasl/slac089

    2022 doi

  4. [12]

    T., Masci , F

    Bolin , B. T., Masci , F. J., Coughlin , M. W., et al. 2025, , 425, 116333, 10.1016/j.icarus.2024.116333

    2025

  5. [13]

    2018, , 70, S5, 10.1093/pasj/psx080

    Bosch , J., Armstrong , R., Bickerton , S., et al. 2018, , 70, S5, 10.1093/pasj/psx080

    2018 doi

  6. [14]

    S., Marshall, P., Anguita, T., et al

    Collaboration, L. S., Marshall, P., Anguita, T., et al. 2017, Science- Driven Optimization of the LSST Observing Strategy , 10.5281/zenodo.842713

    2017 doi

  7. [15]

    J., Angeli , G

    Connolly , A. J., Angeli , G. Z., Chandrasekharan , S., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9150, Modeling, Systems Engineering, and Project Management for Astronomy VI, ed. G. Z. Angeli & P. Dierickx , 915014, 10.1...

    2014 doi

  8. [16]

    Delgado , F., & Reuter , M. A. 2016, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9910, Observatory Operations: Strategies, Processes, and Systems VI, ed. A. B. Peck , R. L. Seaman , & C. R. Benn , 991013, 10.1117/12.2233630

    2016 doi

  9. [17]

    2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol

    Delgado , F., Saha , A., Chandrasekharan , S., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9150, Modeling, Systems Engineering, and Project Management for Astronomy VI, ed. G. Z. Angeli & P. Dierickx , 915015, 10.1117/12.2056898

    2014 doi

  10. [18]

    2020, The Astronomical Journal, 160, 277, 10.3847/1538-3881/abc3bc

    Fedorets, G., Micheli, M., Jedicke, R., et al. 2020, The Astronomical Journal, 160, 277, 10.3847/1538-3881/abc3bc

    2020 doi

  11. [19]

    D., Bianco , F

    Feigelson , E. D., Bianco , F. B., & Bonito , R. 2023, , 268, 11, 10.3847/1538-4365/ace616

    2023 doi

  12. [20]

    E., Yoachim , P., Jones , R

    Gizis , J. E., Yoachim , P., Jones , R. L., Hilligoss , D., & Ji , J. 2022, , 263, 23, 10.3847/1538-4365/ac961f

    2022 doi

  13. [21]

    M., Hivon , E., Banday , A

    G \'o rski , K. M., Hivon , E., Banday , A. J., et al. 2005, , 622, 759, 10.1086/427976

    2005 doi

  14. [22]

    L., Bellm, E

    Graham, M. L., Bellm, E. C., Slater, C. T., Guy, L. P., & the DM System Science Team. 2020, Options for Alert Production in LSST Operations Year 1 . https://dmtn-107.lsst.io/

    2020

  15. [23]

    P., Bellm, E., Blum, B., et al

    Guy, L. P., Bellm, E., Blum, B., et al. 2023, Rubin Observatory Plans for an Early Science Program . https://rtn-011.lsst.io/

    2023

  16. [24]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt , S. J., et al. 2020, Nature, 585, 357, 10.1038/s41586-020-2649-2

    2020 doi

  17. [25]

    2022, in AAS/Division for Planetary Sciences Meeting Abstracts, Vol

    Heinze , A., Eggl , S., Juric , M., et al. 2022, in AAS/Division for Planetary Sciences Meeting Abstracts, Vol. 54, AAS/Division for Planetary Sciences Meeting Abstracts, 504.04

    2022

  18. [26]

    J., Payne, M

    Holman, M. J., Payne, M. J., Blankley, P., Janssen, R., & Kuindersma, S. 2018, The Astronomical Journal, 156, 135, 10.3847/1538-3881/aad69a

    2018 doi

  19. [27]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, 10.1109/MCSE.2007.55

    2007 doi

  20. [28]

    , & the LSST Science Collaboration

    Ivezi \'c , Z . ., & the LSST Science Collaboration. 2013, LSST Science Requirements Document. http://ls.st/LPM-17

    2013

  21. [29]

    , & the SCOC

    Ivezi \'c , Z . ., & the SCOC. 2021, Survey Cadence Optimization Committee's Phase 1 Recommendation. https://pstn-053.lsst.io/

    2021

  22. [30]

    M., Tyson , J

    Ivezi \'c , Z ., Kahn , S. M., Tyson , J. A., et al. 2019, , 873, 111, 10.3847/1538-4357/ab042c

    2019 doi

  23. [31]

    L., Yoachim, P., Ivezic, Z., Neilsen, E

    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, 10.5281/zenodo.4048838

    2020 doi

  24. [32]

    L., Yoachim , P., Chandrasekharan , S., et al

    Jones , R. L., Yoachim , P., Chandrasekharan , S., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9149, Observatory Operations: Strategies, Processes, and Systems V, ed. A. B. Peck , C. R. Benn , & R. L. Seaman , 91490B, 10.11...

    2014 doi

  25. [33]

    L., Slater , C

    Jones , R. L., Slater , C. T., Moeyens , J., et al. 2018, , 303, 181, 10.1016/j.icarus.2017.11.033

    2018 doi

  26. [34]

    2020, Proposed Modifications to Solar System Processing and Data Products

    Juri \'c , M., Eggl , S., Moeyens , J., & Jones , L. 2020, Proposed Modifications to Solar System Processing and Data Products. https://dmtn-087.lsst.io/

    2020

  27. [35]

    2023, Data Products Definition Document

    Juri\' c , M., Axelrod, T., Becker, A., et al. 2023, Data Products Definition Document . https://lse-163.lsst.io/

    2023

  28. [36]

    2007, , 189, 151, 10.1016/j.icarus.2007.01.008

    Kubica , J., Denneau , L., Grav , T., et al. 2007, , 189, 151, 10.1016/j.icarus.2007.01.008

    2007 doi

  29. [37]

    M., Awan , H., et al

    Lochner , M., Scolnic , D. M., Awan , H., et al. 2018, arXiv e-prints, arXiv:1812.00515, 10.48550/arXiv.1812.00515

    2018 doi arXiv

  30. [38]

    2021, , 253, 31, 10.3847/1538-4365/abd62c

    LSST Dark Energy Science Collaboration (LSST DESC) , Abolfathi , B., Alonso , D., et al. 2021, , 253, 31, 10.3847/1538-4365/abd62c

    2021 doi

  31. [39]

    A., Allison , J., et al

    LSST Science Collaboration , Abell , P. A., Allison , J., et al. 2009, arXiv e-prints, arXiv:0912.0201, 10.48550/arXiv.0912.0201

    2009 doi arXiv

  32. [40]

    2017, arXiv e-prints, arXiv:1708.04058

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

    2017 arXiv

  33. [41]

    2018, astropy/astroscrappy: v1.0.5 Zenodo Release , v1.0.5, Zenodo, 10.5281/zenodo.1482019

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

    2018 doi

  34. [42]

    2010, in Python in Science Conference , Austin, Texas, 56--61, 10.25080/Majora-92bf1922-00a

    McKinney, W. 2010, in Python in Science Conference , Austin, Texas, 56--61, 10.25080/Majora-92bf1922-00a

    2010 doi

  35. [43]

    2013, Moving Object Pipeline System Design

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

    2013

  36. [44]

    J., Connolly , A

    Naghib , E., Yoachim , P., Vanderbei , R. J., Connolly , A. J., & Jones , R. L. 2019, , 157, 151, 10.3847/1538-3881/aafece

    2019 doi

  37. [45]

    Rhoads , J. E. 2000, , 112, 703, 10.1086/316559

    2000 doi

  38. [46]

    E., Volk , K., Wen , H., et al

    Schwamb , M. E., Volk , K., Wen , H., et al. 2018 a , arXiv e-prints, arXiv:1812.01149, 10.48550/arXiv.1812.01149

    2018 doi arXiv

  39. [47]

    E., Jones , R

    Schwamb , M. E., Jones , R. L., Chesley , S. R., et al. 2018 b , arXiv e-prints, arXiv:1802.01783, 10.48550/arXiv.1802.01783

    2018 doi arXiv

  40. [48]

    E., Juri \'c , M., Bolin , B

    Schwamb , M. E., Juri \'c , M., Bolin , B. T., et al. 2021, Research Notes of the American Astronomical Society, 5, 143, 10.3847/2515-5172/ac090f

    2021 doi

  41. [49]

    E., Jones, R

    Schwamb, M. E., Jones, R. L., Yoachim, P., et al. 2023, The Astrophysical Journal Supplement Series, 266, 22, 10.3847/1538-4365/acc173

    2023 doi

  42. [50]

    2005, Astronomische Nachrichten, 326, 428, 10.1002/asna.200510364

    Shamir , L. 2005, Astronomische Nachrichten, 326, 428, 10.1002/asna.200510364

    2005 doi

  43. [51]

    2020, Data Management Science Pipelines Design

    Swinbank, J., Axelrod, T., Becker, A., et al. 2020, Data Management Science Pipelines Design . https://ldm-151.lsst.io/

    2020

  44. [52]

    van Dokkum , P. G. 2001, , 113, 1420, 10.1086/323894

    2001 doi

  45. [53]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, 10.1038/s41592-019-0686-2

    2020 doi

  46. [54]

    2024, Lsst-Sims/sims\_ featureScheduler \_runs4.0: Initial Release , Zenodo, 10.5281/zenodo.13840868

    Yoachim , P. 2024, Lsst-Sims/sims\_ featureScheduler \_runs4.0: Initial Release , Zenodo, 10.5281/zenodo.13840868

    2024 doi

  47. [55]

    Z., et al

    Yoachim , P., Coughlin , M., Angeli , G. Z., et al. 2016, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9910, Observatory Operations: Strategies, Processes, and Systems VI, ed. A. B. Peck , R. L. Seaman , & C. R. Benn , 99101A, 10.1117/12.2232947

    2016 doi

  48. [56]

    2019, Journal of Open Source Software, 4, 1298, 10.21105/joss.01298

    Zonca, A., Singer, L., Lenz, D., et al. 2019, Journal of Open Source Software, 4, 1298, 10.21105/joss.01298

    2019 doi

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