Pith. sign in

REVIEW 3 major objections 5 minor 128 references

Predictions of the LSST Solar System Yield: Discovery Rates and Characterizations of Centaurs

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The LSST survey will multiply the known Centaur catalog by 7 to 12, discovering 1,200 to 2,000 icy bodies.

desk verdict A genuinely useful first forecast of LSST Centaur yields, but the abstract's '7-12x' overstates the paper's own tables (~6-7x) and the N0 systematic is under-reported. read the letter →

arxiv 2506.02779 v2 pith:GAY7OVTH submitted 2025-06-03 astro-ph.EP

Joseph Murtagh , Megan E. Schwamb , Stephanie R. Merritt , Pedro H. Bernardinelli , Jacob A. Kurlander , Samuel Cornwall , Mario Jurić , Grigori Fedorets
show 11 more authors
Matthew J. Holman Siegfried Eggl David Nesvorný Kathryn Volk R. Lynne Jones Peter Yoachim Joachim Moeyens Jeremy Kubica Drew Oldag Maxine West Colin Orion Chandler
This is my paper · ORCID
classification astro-ph.EP
keywords CentaursLSSTsurveyyieldpredictionsdiscoveryratesphasecurvessurfacecolorsoutersolarsystemsimulations
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

This paper predicts what the ten-year LSST sky survey will yield for Centaurs, the icy bodies whose orbits cross the giant planets and whose persistence times are short. Using a synthetic population drawn from the leading dynamical model, calibrated to recent debiased survey counts, and forward-modeled through the LSST pointing history with detection and linking effects, the authors estimate 1,170 to 1,967 Centaur discoveries depending on which orbital definition is used. That is a 7- to 12-fold increase over the currently known catalog, with about half of the objects found within the first two years. The simulation also predicts dense coverage: typical discoveries gather roughly 200 observations across six filters, which would support color measurements for hundreds of objects and high-quality phase curves for more than 300. These numbers matter because Centaur surfaces and orbits trace the path from the trans-Neptunian disk to the Jupiter-family comets, and a sample an order of magnitude larger would test models of that transition.

What carries the argument

The machinery is a forward-modeled survey simulation: a synthetic Centaur population drawn from an N-body dynamical model of the trans-Neptunian source, with an absolute-magnitude distribution given by a single power law $N(\le H_r)=N_0\,10^{\alpha_0(H_r-H_0)}$ with slope $\alpha_0=0.42$ and normalization $N_0=21{,}400$ at $H_0=13.7$ for the standard definition, extrapolated to $H_r=20$. Each object is assigned a red or blue surface color spectrum, in a 3:1 blue-to-red ratio, and a linear phase coefficient $\beta=0.071$ mag deg$^{-1}$, then propagated through the LSST's baseline pointing history with a 2.26-degree search radius, CCD gaps, saturation at $m_r=16$, a sigmoid detection-efficiency function, and a linking algorithm that requires three tracklet pairs in a 15-day window. The work this does is to convert an intrinsic population model into concrete predictions of how many objects are discovered, when, how often they are observed, and whether the accumulated photometry is good enough for colors, light curves, and phase curves.

What would settle it

Count the actual Centaur discoveries in the first two to three years of LSST operations, splitting them by the paper's three orbital definitions, and compare with the predicted year-one and year-two totals (roughly 560 to 970 and 680 to 1,200, depending on definition). If the counts fall far short of the power-law extrapolation, or if the brightness distribution of the discovered objects flattens well before $H \sim 20$ instead of following the 0.42 slope, the assumed population size or single power law is wrong.

Watch

Extended reading notes

Core claim

The central claim is that the LSST survey will enlarge the known Centaur population from roughly 200 to 300 objects to about 1,200 to 2,000, with the exact count depending on how a Centaur is defined. For the standard orbital definition (perihelion beyond 7.35 au, semimajor axis inside 30.1 au, Tisserand parameter above 3.05) the prediction is 1,524 discoveries; for a definition that includes orbits down to perihelion 5.2 au it is 1,170; and for a hybrid definition that adds more eccentric objects it is 1,967. Discovery is fast: about 50 percent of each sample is found within two years, and the first year alone produces the bright end of the distribution. Beyond the counts, the paper argues that the survey cadence delivers characterization as well as discovery: a median of roughly 200 observations per object across the $ugrizy$ filters, over 200 Centaurs with high-quality colors in at least three filters, and more than 300 well-defined linear phase curves in $griz$, with median absolute-magnitude uncertainties near 0.03 mag. The discovery totals are insensitive to the assumed red/blue color mixture but scale almost linearly with the assumed population size: adopting a lower normalization, derived from Jupiter-Trojan scaling, lowers the yield by roughly 400 objects.

Load-bearing premise

The load-bearing premise is that the intrinsic Centaur population follows a single power-law brightness distribution with slope 0.42, normalized to about 21,400 objects brighter than $H=13.7$, and that this same power law can be extrapolated to $H=20$; the predicted discovery counts scale nearly linearly with that normalization, so a smaller true population would shrink the yield by hundreds of objects.

Editorial extensions

If this is right

  • About half of the predicted discoveries, roughly 560 to 970 objects depending on definition, would be linked within the first year, so follow-up and orbit-validation programs for distant small bodies must be ready to absorb that influx at survey start.
  • Hundreds of Centaurs with at least three high-quality filter colors would become available, roughly an order of magnitude more than the current samples used to establish the red/blue color bimodality.
  • More than 300 well-constrained linear phase curves in $griz$ would improve median absolute-magnitude uncertainties to about 0.03 mag, an order of magnitude better than most current catalog values.
  • The roughly 30 to 50 Centaurs that pass through a deep-drilling field near the ecliptic would accumulate many hundreds to thousands of observations, making them prime targets for dense light curves, rotation periods, and activity searches.
  • If the yield comes in near the predicted values, the ratio of discoveries across the three definitions will directly test which orbital definition best matches the underlying dynamical population.

Reading between the lines

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

  • If the early yields match predictions, the discovery timeline implies the survey's moving-object linking pipeline will encounter a surge of slow-moving objects in the first year; comparing the actual linking efficiency against the simulated one would let observers tune the model's detection-efficiency parameters.
  • The predicted hundreds of multi-filter colors would let observers search for a correlation between color and orbital state among Centaurs, potentially linking the color bimodality to dynamical age or surface processing in a way the small current samples cannot.
  • The power-law extrapolation to $H=20$ is a strong assumption; the survey itself will measure where the Centaur size distribution breaks, so the same simulator could be re-run with the observed break to bracket the true population.
  • The finding that only roughly 30 to 50 Centaurs enter a deep-drilling field suggests dedicated mini-surveys or targeted follow-up along the ecliptic could complement the main survey for objects that would otherwise be missed.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. This paper presents the first forward-modeling predictions for the LSST Centaur discovery yield, using the Sorcha survey simulator with the v4.0 LSST baseline cadence and a Centaur population model based on the Nesvorný et al. (2019) dynamical simulations, calibrated to OSSOS and Pan-STARRS constraints via the G08 definition. The authors simulate three Centaur orbital definitions (G08, S19, Hybrid), predict 1524, 1170, and 1967 discoveries over ten years, and characterize the expected timing of discoveries, numbers of observations per object, DDF enhancements, color measurements, and phase curves. They conclude that LSST will increase the known MPC Centaur sample by roughly an order of magnitude and enable qualitatively new characterization studies.

Significance. If the results hold, this is a valuable and timely forecast: it provides the first quantitative LSST Centaur yield predictions, identifies the early-survey discovery window, and quantifies the expected volume of color, light-curve, and phase-curve data. The forward-modeling approach is appropriate and largely grounded in independently calibrated inputs (dynamical model, OSSOS/Pan-STARRS normalization, published color and phase-function data), and the paper tests sensible alternatives (color fractions, cadence variants, and alternative population scalings). The open-source simulator and public cadence inputs are strengths. The main weaknesses are that the headline fold-increase claim is inconsistent with the paper's own Table 3, and the central discovery range in the abstract does not incorporate the stated N0 systematic uncertainty, which is comparable in size to the definition-to-definition spread.

major comments (3)
  1. [Abstract; Section 3.1; Table 3] The abstract and conclusions state a '~7-12 fold increase' in the known MPC Centaur population, but the paper's own Table 3 gives MPC populations of 215, 186, and 288 and predicted discoveries of 1524, 1170, and 1967, which correspond to ratios of 7.1, 6.3, and 6.8, i.e., a 6-7 fold increase. The 12-fold figure appears to arise from comparing 1524 to the 168 Centaurs in Volk & Van Laerhoven (2024) cited in the Introduction, but that denominator is not used in Table 3 and is not stated in the abstract or conclusions. Section 3.1 itself correctly notes 'this shows a ~6-7 fold increase' when using the MPC counts. The abstract and conclusions should be revised to use the same comparison as Table 3, or should explicitly identify the denominator used for the 12-fold claim.
  2. [Section 2.3.2; Section 4] The predicted yields scale almost linearly with the adopted normalization N0 = 21,400, whose stated uncertainty is +3400/-2800 (Kurlander et al. 2025), yet this systematic uncertainty is not propagated into the abstract's headline range of '~1200-2000'. The paper's own sensitivity test using the alternative N0 = 15,600 reduces the G08 yield by about 400 objects (roughly 25%), which is much larger than the quoted 5-8% run-to-run variation. The abstract should either present the 1200-2000 range as conditional on the adopted N0, or fold the N0 systematic into a full error budget alongside the definition-dependent spread.
  3. [Section 3.4; Table 5] The phase-curve quality metrics underlying the abstract's 'over 300 well-defined phase curves' claim are applied to simulated photometry, but the paper does not demonstrate that the linear-fit pipeline with the stated cuts (>=25 points, phase-angle range >=3 deg, sigma_H <=0.1 mag, sigma_beta <=0.02 mag/deg) recovers the input beta without significant bias when the available phase-angle range is only ~3-14 deg. Because the phase-curve predictions rest on this pipeline, an injection-recovery test on simulated objects with known beta and realistic photometric scatter should be reported, including the bias and scatter of the recovered beta and H.
minor comments (5)
  1. [Section 2.2] The statement that the one-snap and two-snap cadence variants give consistent results is not quantified; reporting the discovery totals for both variants would make the claim verifiable.
  2. [Section 3.2; Table 4] The median u-band observation count of zero for all three samples is striking and should be interpreted in the text in terms of the adopted colors and cadence, rather than appearing only as a table entry.
  3. [Figure 11] The bar labels in Figure 11 are small and the three H cuts per definition are difficult to distinguish by eye; a small table of the counts would improve readability.
  4. [Section 2.3.4] The phase coefficient beta is assigned uniformly across all filters, while Table 5 reports per-filter phase-curve counts and uncertainties; the text should note explicitly that the input beta is filter-independent and discuss any effect this simplification may have on per-filter comparisons.
  5. [References] Core simulation tools are cited as 'in press' or 'submitted' (Merritt et al., Holman et al., Robinson et al.); if available, adding arXiv identifiers or accepted-version details would aid reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the LSST Centaur yield is a forward simulation from externally calibrated population models and an independent survey simulator; the paper's '7-12x' wording conflicts with its own Table 3 ratios, but that is a numerical inconsistency, not a circular derivation.

full rationale

The claimed predictions (1524, 1170, and 1967 discoveries for G08, S19, and Hybrid) are outputs of a forward survey simulation using Sorcha, applied to synthetic Centaur populations whose orbital distributions, absolute-magnitude slope (alpha=0.42), normalization (N0=21,400; Kurlander et al. 2025), colors, and phase coefficients are fixed before the LSST simulation is run. None of these inputs is fitted to LSST data or to the quantities being predicted, so the yield is not equivalent to the input model by construction. The normalization N0 is the dominant scale factor, but the paper explicitly propagates the OSSOS-calibrated N0=21,000 and the Jupiter-Trojan-scaled N0=15,600 alternatives and reports the resulting changes (roughly 30 and 400 detections, respectively), so the conclusion does not reduce to a single fitted parameter. The cited Sorcha code and the Kurlander et al. (2025) Pan-STARRS calibration are external, published, and open-source tools/measurements rather than circular support, and the paper's Section 4 limitations transparently state the dependence on the input population size. The abstract's '~7-12 fold increase' is inconsistent with the paper's own Table 3 (1524/215=7.1, 1170/186=6.3, 1967/288=6.8, i.e., approximately 6-7 fold); this is a numerical and presentational defect in the headline claim, not a circularity, and it is noted here so it is not mistaken for an independent circularity finding.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The predictions rest on a chain of external models and fitted constants. The total yield scales almost linearly with N0=21,400 (G08), whose uncertainty is +3400/-2800, and the H distribution is a single power law extrapolated to H=20 without a faint-end calibration. The orbital distribution, phase function, colors, cadence simulation, and linking assumptions are all adopted from prior work or fixed by hand. The paper tests some alternatives but does not combine them into a formal error budget.

free parameters (5)
  • N0 (population normalization) = 21400 (G08); 21654 (Hybrid); S19 via orbital overlap
    Cumulative number of Centaurs with H<=13.7 from debiased Pan-STARRS (Kurlander et al. 2025) and OSSOS. The final LSST yield is almost directly proportional to this normalization.
  • alpha (H distribution slope) = 0.42
    Slope of the cumulative absolute magnitude distribution from OSSOS (Shankman et al. 2016, Lawler et al. 2018b). It is extrapolated to H=20 and controls how many faint objects exist in the synthetic population.
  • H0 (reference absolute magnitude) = 13.7
    Reference absolute magnitude for N0, from the Kurlander et al. 2025 calibration. Used in Equation 3 to set the size of the simulated population.
  • beta (linear phase coefficient) = 0.071 mag/deg
    Mean linear phase coefficient from 23 Centaur measurements, applied uniformly to all objects and all filters. It sets apparent magnitudes through Equation 5 and therefore affects detectability.
  • blue:red color fraction = 75:25
    Fraction of Bienor-like to Pholus-like colors from Wong & Brown 2017. Sensitivity tests at 90:10 and 10:90 change yields by about 10 objects, so this is a minor parameter.
assumptions (6)
  • domain assumption The Nesvorny et al. (2019) N-body integration provides an accurate steady-state orbital distribution of the Centaur population.
    Section 2.3.1 uses the output of this model as the orbital input for all synthetic objects. If the true orbital distribution differs, the sky coverage and discoverability of the population would change.
  • domain assumption A single power-law absolute magnitude distribution (Equation 3) is valid up to H=20, including the unconstrained faint end.
    Section 2.3.2 extrapolates a slope calibrated to OSSOS/Pan-STARRS to much fainter magnitudes. No independent constraint on the faint-end slope is available.
  • domain assumption The LSST v4.0 one-snap cadence simulation is a faithful representation of the actual ten-year survey.
    Section 2.2 bases all results on this simulated cadence; the paper notes year 1 downtime differs and that two-snap gives similar results, but weather, scheduler, and hardware behavior remain modeled rather than real.
  • domain assumption The Rubin SSP linking algorithm achieves its nominal detection and linking efficiency, and all associations and precoveries are perfect.
    Section 2.1 assumes 95 percent linking of qualifying tracklets; Section 3.2 and Table 4 state the observation counts assume perfect association and precovery. Section 4 caveats template generation, which affects the first year.
  • domain assumption A linear phase function with beta=0.071 mag/deg in all filters describes Centaur brightness variation with phase angle.
    Section 2.3.4 assigns a uniform phase coefficient because the measured sample is small, while acknowledging the coefficient is filter dependent.
  • domain assumption Two canonical spectra, Pholus and Bienor, with a 3:1 blue-to-red fraction, represent the color diversity of the Centaur population for detection purposes.
    Section 2.3.3 constructs colors from these two spectra; the paper tests extreme color fractions and finds a small effect on yield.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Predictions of the LSST Solar System Yield: Discovery Rates and Characterizations of Centaurs." pith.science (2026). https://pith.science/paper/GAY7OVTH

@misc{pith2026250602779,
  author       = {Pith},
  title        = {Pith review of: Predictions of the LSST Solar System Yield: Discovery Rates and Characterizations of Centaurs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GAY7OVTH}},
  note         = {Machine review of arXiv:2506.02779}
}
abstract

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will start by the end of 2025 and operate for ten years, offering billions of observations of the southern night sky. One of its main science goals is to create an inventory of the Solar System, allowing for a more detailed understanding of small body populations including the Centaurs, which will benefit from the survey's high cadence and depth. In this paper, we establish the first discovery limits for Centaurs throughout the LSST's decade-long operation using the best available dynamical models. Using the survey simulator $\texttt{Sorcha}$, we predict a $\sim$7-12 fold increase in Centaurs in the Minor Planet Center (MPC) database, reaching $\sim$1200-2000 (dependent on definition) by the end of the survey - about 50$\%$ of which are expected within the first 2 years. Approximately 30-50 Centaurs will be observed twice as frequently as they fall within one of the LSST's Deep Drilling Fields (DDF) for on average only up to two months. Outside of the DDFs, Centaurs will receive $\sim$200 observations across the $\textit{ugrizy}$ filter range, facilitating searches for cometary-like activity through PSF extension analysis, as well as fitting light-curves and phase curves for color determination. Regardless of definition, over 200 Centaurs will achieve high-quality color measurements across at least three filters in the LSST's six filters. These observations will also provide over 300 well-defined phase curves in the $\textit{griz}$ bands, improving absolute magnitude measurements to a precision of 0.2 mags.

Figures

Figures reproduced from arXiv: 2506.02779 by the authors.

Figure 1
Figure 1. Skymap of the number of visits across all filters of the 10 year LSST survey cadence based on the one snap v4.0 simulation (SCOC 2024). The main Wide-Fast-Deep (WFD) survey making up ∼80% of the total survey time will receive ∼800 visits per pointing. There are additional ”Mini” surveys highlighted that make up ∼3-10% of the survey time. These cover differing areas of the sky, including the Northern Ecliptic Spur (N… view at source ↗
Figure 2
Figure 2. Orbital space 2D histograms for the three dynamical classifications of the input Centaur models used in this work; namely the G08 sample on the leftmost column, the S19 sample on the middle column, and the Hybrid sample in the rightmost column. The top row shows histograms of Centaur eccentricity as a function of semimajor axis, whereas the bottom row shows histograms of Centaur inclination as a function of periheli… view at source ↗
Figure 3
Figure 3. Cumulative histogram of the model absolute magnitude H distribution of objects with H less than a given absolute magnitude Hr for the G08 sample. Inset represents a zoom-in of the bright end of the same distribution where H < 11. 2003; Peixinho et al. 2003; Barucci et al. 2005; Perna et al. 2010; Wong & Brown 2017), owing in part to their shared dynamical history to the small hot TNO populations (Duncan & Levison 19… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Original reflectance spectra of Pholus (left, Fornasier et al. 2009) and Bienor (right, Alvarez￾Candal et al. 2008), normalized at 550nm. Overlaid are the new extrapolated, smoothed ‘spectra’ that are used to convert to SED’s and obtain color estimates. Below are the L…
Figure 5
Figure 5. Figure 5: Histogram showing the number of 10,000 simulations of each trial N0 value for the Hybrid definition that match the G08 sample scaling of N (H <13.7) = 21,400. The dashed line represents the mean value of the distribution, and the shaded region is the 95% confidence int…
Figure 6
Figure 6. Figure 6: Cumulative histogram (with a bin size of one day) of the discovery rates of the different Centaur definitions over the 10 year LSST lifetime. Red dashed lines demark yearly boundaries for the survey, with labels above each block to highlight the year of operation. We h…
Figure 7
Figure 7. Figure 7: Scatter plots of the orbital spaces covered by discovered objects, similar to [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: Histograms of the ecliptic latitudes for each of the G08, S19, and Hybrid definitions respectively at the time of each object’s discovery. (Fraser et al. 2022; Guilbert-Lepoutre et al. 2023). More recent thermal and dynamical modeling from Guilbert-Lepoutre et al. (202…
Figure 9
Figure 9. Figure 9 [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Cumulative histograms of the number of observations each objected detected in each sample will obtain, broken down by filter - excluding those observations that fall within the COSMOS DDF. Note that for all of these plots the x-axis is cut off at 250 for readability, …
Figure 11
Figure 11. Figure 11: Number of Centaurs which would obtain at least three colors in ugrizy. Each bar represents the fraction of each sub-sample of discovered objects that would pass the color metrics defined in Schwamb et al. (2023), with the actual number of Centaurs this represents abov…
Figure 12
Figure 12. Figure 12: Example phase curves across all ugrizy filters for a randomly sampled object from the G08 sample. There are no uy band phase curves here as they do not have enough observations passing metrics for quality phase curves. All filters that do pass the metrics have been ov…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

128 extracted references · 6 canonical work pages

  1. [1]

    2018, Planet

    Acton, C., Bachman, N., Semenov, B., & Wright, E. 2018, Planet. Space Sci., 150, 9, doi: 10.1016/j.pss.2017.02.013

    doi:10.1016/j.pss.2017.02.013 2018

  2. [2]

    Acton, C. H. 1996, Planet. Space Sci., 44, 65, doi: 10.1016/0032-0633(95)00107-7

    doi:10.1016/0032-0633(95)00107-7 1996

  3. [3]

    R., Gulbis, A

    Adams, E. R., Gulbis, A. A. S., Elliot, J. L., et al. 2014, AJ, 148, 55, doi: 10.1088/0004-6256/148/3/55

    doi:10.1088/0004-6256/148/3/55 2014

  4. [4]

    2019, MNRAS, 488, 3035, doi: 10.1093/mnras/stz1880

    Alvarez-Candal, A., Ayala-Loera, C., Gil-Hutton, R., et al. 2019, MNRAS, 488, 3035, doi: 10.1093/mnras/stz1880

    doi:10.1093/mnras/stz1880 2019

  5. [5]

    A., de Bergh, C., & Merlin, F

    Alvarez-Candal, A., Fornasier, S., Barucci, M. A., de Bergh, C., & Merlin, F. 2008, A&A, 487, 741, doi: 10.1051/0004-6361:200809705

    doi:10.1051/0004-6361:200809705 2008

  6. [6]

    L., et al

    Alvarez-Candal, A., Pinilla-Alonso, N., Ortiz, J. L., et al. 2016, A&A, 586, A155, doi: 10.1051/0004-6361/201527161

    doi:10.1051/0004-6361/201527161 2016

  7. [7]

    2020, The Journal of Open Source Software, 5, 2050, doi: 10.21105/joss.02050

    Annex, A., Pearson, B., Seignovert, B., et al. 2020, The Journal of Open Source Software, 5, 2050, doi: 10.21105/joss.02050

    doi:10.21105/joss.02050 2020

  8. [8]

    A., et al

    Annis, J., Soares-Santos, M., Strauss, M. A., et al. 2014, ApJ, 794, 120, doi: 10.1088/0004-637X/794/2/120 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f Astropy Coll...

    doi:10.1088/0004-637x/794/2/120 2014

Show all 128 references
  1. [9]

    L., et al

    Ayala-Loera, C., Alvarez-Candal, A., Ortiz, J. L., et al. 2018, MNRAS, 481, 1848, doi: 10.1093/mnras/sty2363

    2018 doi

  2. [10]

    2006, A&A, 450, 1239, doi: 10.1051/0004-6361:20054518

    Bagnulo, S., Boehnhardt, H., Muinonen, K., et al. 2006, A&A, 450, 1239, doi: 10.1051/0004-6361:20054518

    2006 doi

  3. [11]

    L., & Malhotra, R

    Bailey, B. L., & Malhotra, R. 2009, Icarus, 203, 155, doi: 10.1016/j.icarus.2009.03.044

    2009 doi

  4. [12]

    T., Gladman, B

    Bannister, M. T., Gladman, B. J., Kavelaars, J. J., et al. 2018, ApJS, 236, 18, doi: 10.3847/1538-4365/aab77a

    2018 doi

  5. [13]

    M., Brown, M

    Barkume, K. M., Brown, M. E., & Schaller, E. L. 2008, AJ, 135, 55, doi: 10.1088/0004-6256/135/1/55

    2008 doi

  6. [14]

    A., Alvarez-Candal, A., Merlin, F., et al

    Barucci, M. A., Alvarez-Candal, A., Merlin, F., et al. 2011, Icarus, 214, 297, doi: 10.1016/j.icarus.2011.04.019

    2011 doi

  7. [15]

    A., Belskaya, I

    Barucci, M. A., Belskaya, I. N., Fulchignoni, M., & Birlan, M. 2005, AJ, 130, 1291, doi: 10.1086/431957

    2005 doi

  8. [16]

    2008, in The Solar System Beyond Neptune, ed

    Merlin, F. 2008, in The Solar System Beyond Neptune, ed. M. A. Barucci, H. Boehnhardt, D. P. Cruikshank, A. Morbidelli, & R. Dotson, 143–160

    2008

  9. [17]

    A., & Merlin, F

    Barucci, M. A., & Merlin, F. 2020, in The Trans-Neptunian Solar System, ed. D. Prialnik, M. A. Barucci, & L. Young, 109–126, doi: 10.1016/B978-0-12-816490-7.00005-9

    2020 doi

  10. [18]

    M., Grav, T., Blauvelt, E., et al

    Bauer, J. M., Grav, T., Blauvelt, E., et al. 2013, ApJ, 773, 22, doi: 10.1088/0004-637X/773/1/22

    2013 doi

  11. [19]

    G., & Muinonen, K

    Shkuratov, Y. G., & Muinonen, K. 2008, in The Solar System Beyond Neptune, ed. M. A

    2008

  12. [20]

    H., Bernstein, G

    Bernardinelli, P. H., Bernstein, G. M., Sako, M., et al. 2022, ApJS, 258, 41, doi: 10.3847/1538-4365/ac3914

    2022 doi

  13. [21]

    B., Ivezi´ c,ˇZ., Jones, R

    Bianco, F. B., Ivezi´ c,ˇZ., Jones, R. L., et al. 2022, ApJS, 258, 1, doi: 10.3847/1538-4365/ac3e72

    2022 doi

  14. [22]

    Brown, M. E. 2012, Annual Review of Earth and Planetary Sciences, 40, 467, doi: 10.1146/annurev-earth-042711-105352

    2012 doi

  15. [23]

    W., Tholen, D

    Buie, M. W., Tholen, D. J., & Horne, K. 1992, Icarus, 97, 211, doi: 10.1016/0019-1035(92)90129-U LSST Centaur Discoveries27

    1992 doi

  16. [24]

    C., et al

    Cabral, N., Guilbert-Lepoutre, A., Fraser, W. C., et al. 2019, A&A, 621, A102, doi: 10.1051/0004-6361/201834021

    2019 doi

  17. [25]

    O., Trujillo, C

    Chandler, C. O., Trujillo, C. A., Oldroyd, W. J., et al. 2024, AJ, 167, 156, doi: 10.3847/1538-3881/ad1de2

    2024 doi

  18. [26]

    R., & Veres, P

    Chesley, S. R., & Veres, P. 2017, arXiv e-prints, arXiv:1705.06209, doi: 10.48550/arXiv.1705.06209

    2017 doi arXiv

  19. [27]

    J., Angeli, G

    Connolly, A. J., Angeli, G. Z., Chandrasekharan, S., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference

    2014

  20. [28]

    9150, Modeling, Systems Engineering, and Project Management for Astronomy VI, ed

    Series, Vol. 9150, Modeling, Systems Engineering, and Project Management for Astronomy VI, ed. G. Z. Angeli & P. Dierickx, 915014, doi: 10.1117/12.2054953 da Costa-Luis, C., Larroque, S. K., Altendorf, K., et al. 2023, tqdm: A fast, Extensible Progress Bar for Python and CLI, ...

    2023 doi

  21. [29]

    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, doi: 10.1117/12.2233630

    2016 doi

  22. [30]

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

    Delgado, F., Saha, A., Chandrasekharan, S., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference

    2014

  23. [31]

    9150, Modeling, Systems Engineering, and Project Management for Astronomy VI, ed

    Series, Vol. 9150, Modeling, Systems Engineering, and Project Management for Astronomy VI, ed. G. Z. Angeli & P. Dierickx, 915015, doi: 10.1117/12.2056898 Di Sisto, R. P., & Brunini, A. 2007, Icarus, 190, 224, doi: 10.1016/j.icarus.2007.02.012 Di Sisto, R. P., & Rossignoli, N....

    2007 doi

  24. [32]

    M., Schwamb, M

    Dobson, M. M., Schwamb, M. E., Benecchi, S. D., et al. 2023, PSJ, 4, 75, doi: 10.3847/PSJ/acc463

    2023 doi

  25. [33]

    M., Schwamb, M

    Dobson, M. M., Schwamb, M. E., Fitzsimmons, A., et al. 2024, PSJ, 5, 165, doi: 10.3847/PSJ/ad543c

    2024 doi

  26. [34]

    2015, SSRv, 197, 191, doi: 10.1007/s11214-015-0223-2

    Dones, L., Brasser, R., Kaib, N., & Rickman, H. 2015, SSRv, 197, 191, doi: 10.1007/s11214-015-0223-2

    2015 doi

  27. [35]

    2004, in Comets II, ed

    Duncan, M., Levison, H., & Dones, L. 2004, in Comets II, ed. M. C. Festou, H. U. Keller, & H. A. Weaver, 193

    2004

  28. [36]

    J., & Levison, H

    Duncan, M. J., & Levison, H. F. 1997, Science, 276, 1670, doi: 10.1126/science.276.5319.1670

    1997

  29. [37]

    L., Kern, S

    Elliot, J. L., Kern, S. D., Clancy, K. B., et al. 2005, AJ, 129, 1117, doi: 10.1086/427395 Emel’yanenko, V. V., Asher, D. J., & Bailey, M. E. 2005, MNRAS, 361, 1345, doi: 10.1111/j.1365-2966.2005.09267.x

    2005

  30. [38]

    L., Juri´ c, M., & Jedicke, R

    Fedorets, G., Granvik, M., Jones, R. L., Juri´ c, M., & Jedicke, R. 2020, Icarus, 338, 113517, doi: 10.1016/j.icarus.2019.113517 Fern´ andez, Y. R., Kelley, M. S., Lamy, P. L., et al. 2013, Icarus, 226, 1138, doi: 10.1016/j.icarus.2013.07.021

    2020

  31. [39]

    A., de Bergh, C., et al

    Fornasier, S., Barucci, M. A., de Bergh, C., et al. 2009, A&A, 508, 457, doi: 10.1051/0004-6361/200912582

    2009 doi

  32. [40]

    2014, A&A, 568, L11, doi: 10.1051/0004-6361/201424439

    Fornasier, S., Lazzaro, D., Alvarez-Candal, A., et al. 2014, A&A, 568, L11, doi: 10.1051/0004-6361/201424439

    2014 doi

  33. [41]

    C., & Brown, M

    Fraser, W. C., & Brown, M. E. 2012, ApJ, 749, 33, doi: 10.1088/0004-637X/749/1/33

    2012 doi

  34. [42]

    2014, ApJ, 782, 100, doi: 10.1088/0004-637X/782/2/100

    Parker, A., & Batygin, K. 2014, ApJ, 782, 100, doi: 10.1088/0004-637X/782/2/100

    2014 doi

  35. [43]

    C., Dones, L., Volk, K., Womack, M., & Nesvorn´ y, D

    Fraser, W. C., Dones, L., Volk, K., Womack, M., & Nesvorn´ y, D. 2022, arXiv e-prints, arXiv:2210.16354, doi: 10.48550/arXiv.2210.16354

    2022 doi arXiv

  36. [44]

    C., Pike, R

    Fraser, W. C., Pike, R. E., Marsset, M., et al. 2023, PSJ, 4, 80, doi: 10.3847/PSJ/acc844

    2023 doi

  37. [45]

    G., & Vanlaerhoven, C

    Gladman, B., Marsden, B. G., & Vanlaerhoven, C. 2008, in The Solar System Beyond Neptune, ed. M. A. Barucci, H. Boehnhardt, D. P

    2008

  38. [46]

    Morbidelli, & R

    Cruikshank, A. Morbidelli, & R. Dotson, 43–57 G’orski, K. M., Hivon, E., Banday, A. J., et al. 2005, ApJ, 622, 759, doi: 10.1086/427976

    2005 doi

  39. [47]

    K., & Spahr, T

    Grav, T., Mainzer, A. K., & Spahr, T. 2016, AJ, 151, 172, doi: 10.3847/0004-6256/151/6/172

    2016 doi

  40. [48]

    2009, Icarus, 201, 272, doi: 10.1016/j.icarus.2008.12.023

    Guilbert, A., Alvarez-Candal, A., Merlin, F., et al. 2009, Icarus, 201, 272, doi: 10.1016/j.icarus.2008.12.023

    2009 doi

  41. [49]

    N., & Nesvorny, D

    Guilbert-Lepoutre, A., Gkotsinas, A., Raymond, S. N., & Nesvorny, D. 2023, ApJ, 942, 92, doi: 10.3847/1538-4357/acaa3a

    2023 doi

  42. [50]

    P., Bechtol, K., Bellm, E., et al

    Guy, L. P., Bechtol, K., Bellm, E., et al. 2024, Rubin Observatory Plans for an Early Science Program, https://rtn-011.lsst.io/

    2024

  43. [51]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2 28Murtagh et al

    2020 doi

  44. [52]

    J., Akmal, A., Farnocchia, D., et al

    Holman, M. J., Akmal, A., Farnocchia, D., et al. 2023, PSJ, 4, 69, doi: 10.3847/PSJ/acc9a9

    2023 doi

  45. [53]

    J., & Wisdom, J

    Holman, M. J., & Wisdom, J. 1993, AJ, 105, 1987, doi: 10.1086/116574

    1993 doi

  46. [54]

    J., Seligman, D

    Hoover, D. J., Seligman, D. Z., & Payne, M. J. 2022, PSJ, 3, 71, doi: 10.3847/PSJ/ac58fe

    2022 doi

  47. [55]

    Denneau, L., & Heinze, A. N. 2021, Icarus, 354, 114019, doi: 10.1016/j.icarus.2020.114019

    2021

  48. [56]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55 Ivezi´ c, Z., & the LSST Science Collaboration. 2013, The LSST System Science Requirements

    2007 doi

  49. [57]

    M., Tyson, J

    Document, Cadence Note LPM-17, http://ls.st/LPM-17 Ivezi´ c,ˇZ., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111, doi: 10.3847/1538-4357/ab042c

    2019 doi

  50. [58]

    2002, in Asteroids III, 71–87

    Jedicke, R., Larsen, J., & Spahr, T. 2002, in Asteroids III, 71–87

    2002

  51. [59]

    2009, AJ, 137, 4296, doi: 10.1088/0004-6256/137/5/4296

    Jewitt, D. 2009, AJ, 137, 4296, doi: 10.1088/0004-6256/137/5/4296

    2009 doi

  52. [60]

    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, doi: 10.5281/zenodo.4048837

    2020 doi

  53. [61]

    L., Chesley, S

    Jones, R. L., Chesley, S. R., Connolly, A. J., et al. 2009, Earth Moon and Planets, 105, 101, doi: 10.1007/s11038-009-9305-z

    2009 doi

  54. [62]

    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

    2014

  55. [63]

    Peck, C. R. Benn, & R. L. Seaman, 91490B, doi: 10.1117/12.2056835

    doi

  56. [64]

    L., Slater, C

    Jones, R. L., Slater, C. T., Moeyens, J., et al. 2018, Icarus, 303, 181, doi: 10.1016/j.icarus.2017.11.033 Juri´ c, M., Eggl, S., Moeyens, J., & Jones, R. L. 2020, Proposed Modifications to Solar System Processing and Data Products, Cadence Note DMTN-087, https://dmtn-087.lsst...

    2018 doi

  57. [65]

    W., et al

    Kareta, T., Volk, K., Noonan, J. W., et al. 2020, Research Notes of the American Astronomical Society, 4, 74, doi: 10.3847/2515-5172/ab963c

    2020 doi

  58. [66]

    M., Schambeau, C., et al

    Kareta, T., Woodney, L. M., Schambeau, C., et al. 2021, PSJ, 2, 48, doi: 10.3847/PSJ/abe23d

    2021 doi

  59. [67]

    2016, in Positioning and Power in Academic Publishing: Players, Agents and Agendas (IOS Press), 87–90

    Kluyver, T., Ragan-Kelley, B., P´ erez, F., et al. 2016, in Positioning and Power in Academic Publishing: Players, Agents and Agendas (IOS Press), 87–90. https://eprints.soton.ac.uk/403913/

    2016

  60. [68]

    2016, Icarus, 271, 314, doi: 10.1016/j.icarus.2016.01.037

    Kulyk, I., Korsun, P., Rousselot, P., Afanasiev, V., & Ivanova, O. 2016, Icarus, 271, 314, doi: 10.1016/j.icarus.2016.01.037

    2016 doi

  61. [69]

    A., Holman, M

    Kurlander, J. A., Holman, M. J., Bernardinelli, P. H., et al. 2025, AJ, 169, 73, doi: 10.3847/1538-3881/ad9a58

    2025 doi

  62. [70]

    Kurucz, R. L. 2005, Memorie della Societa Astronomica Italiana Supplementi, 8, 189

    2005

  63. [71]

    K., Pitrou, A., & Seibert, S

    Lam, S. K., Pitrou, A., & Seibert, S. 2015, in Proc. Second Workshop on the LL VM Compiler Infrastructure in HPC, 1–6, doi: 10.1145/2833157.2833162

    2015

  64. [72]

    M., Kavelaars, J

    Lawler, S. M., Kavelaars, J. J., Alexandersen, M., et al. 2018a, Frontiers in Astronomy and Space Sciences, 5, 14, doi: 10.3389/fspas.2018.00014

    2018

  65. [73]

    M., Shankman, C., Kavelaars, J

    Lawler, S. M., Shankman, C., Kavelaars, J. J., et al. 2018b, AJ, 155, 197, doi: 10.3847/1538-3881/aab8ff

    doi

  66. [74]

    F., & Duncan, M

    Levison, H. F., & Duncan, M. J. 1997, Icarus, 127, 13, doi: 10.1006/icar.1996.5637

    1997

  67. [75]

    2024, ApJL, 960, L8, doi: 10.3847/2041-8213/ad1606 LSST Science Collaboration, Abell, P

    Lilly, E., Jevˇ c´ ak, P., Schambeau, C., et al. 2024, ApJL, 960, L8, doi: 10.3847/2041-8213/ad1606 LSST Science Collaboration, Abell, P. A., Allison, J., et al. 2009, LSST Science Book, Version 2.0. https://arxiv.org/abs/0912.0201 LSST Science Collaboration, Marshall, P.,

    2024 arXiv

  68. [76]

    2017, arXiv e-prints, arXiv:1708.04058, doi: 10.48550/arXiv.1708.04058 Mazzotta Epifani, E., Dotto, E., Ieva, S., et al

    Anguita, T., et al. 2017, arXiv e-prints, arXiv:1708.04058, doi: 10.48550/arXiv.1708.04058 Mazzotta Epifani, E., Dotto, E., Ieva, S., et al. 2018, A&A, 620, A93, doi: 10.1051/0004-6361/201731224

    2017 doi arXiv

  69. [77]

    2019, The Journal of Open Source Software, 4, 1426, doi: 10.21105/joss.01426 LSST Centaur Discoveries29

    Mommert, M., Kelley, M., de Val-Borro, M., et al. 2019, The Journal of Open Source Software, 4, 1426, doi: 10.21105/joss.01426 LSST Centaur Discoveries29

    2019 doi

  70. [78]

    L., & Axelrod, T

    Myers, J., Jones, R. 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

    2013

  71. [79]

    J., & Jones, R

    Connolly, A. J., & Jones, R. L. 2019, AJ, 157, 151, doi: 10.3847/1538-3881/aafece Nesvorn´ y, D., Vokrouhlick´ y, D., Stern, A. S., et al. 2019, AJ, 158, 132, doi: 10.3847/1538-3881/ab3651 pandas development team, T. 2020, pandas-dev/pandas: Pandas, latest, Zenodo, doi: 10.528...

    2019 doi

  72. [80]

    2012, A&A, 546, A86, doi: 10.1051/0004-6361/201219057

    Gafeira, R., & Lacerda, P. 2012, A&A, 546, A86, doi: 10.1051/0004-6361/201219057

    2012 doi

  73. [81]

    2003, A&A, 410, L29, doi: 10.1051/0004-6361:20031420

    Peixinho, N., Doressoundiram, A., Delsanti, A., et al. 2003, A&A, 410, L29, doi: 10.1051/0004-6361:20031420

    2003 doi

  74. [82]

    C., et al

    Peixinho, N., Thirouin, A., Tegler, S. C., et al. 2020, in The Trans-Neptunian Solar System, ed. D. Prialnik, M. A. Barucci, & L. Young, 307–329, doi: 10.1016/B978-0-12-816490-7.00014-X

    2020 doi

  75. [83]

    A., Fornasier, S., et al

    Perna, D., Barucci, M. A., Fornasier, S., et al. 2010, A&A, 510, A53, doi: 10.1051/0004-6361/200913654

    2010 doi

  76. [84]

    2008, in The Solar System Beyond Neptune, ed

    Loredo, T. 2008, in The Solar System Beyond Neptune, ed. M. A. Barucci, H. Boehnhardt, D. P. Cruikshank, A. Morbidelli, & R. Dotson, 71–87

    2008

  77. [85]

    1992, ApJ, 388, 196, doi: 10.1086/171143 PyTables Developers Team

    Prialnik, D. 1992, ApJ, 388, 196, doi: 10.1086/171143 PyTables Developers Team. 2002, PyTables: Hierarchical Datasets in Python. https://www.pytables.org/

    1992 doi

  78. [86]

    2012, AJ, 144, 140, doi: 10.1088/0004-6256/144/5/140

    Tourtellotte, S. 2012, AJ, 144, 140, doi: 10.1088/0004-6256/144/5/140

    2012 doi

  79. [87]

    L., Schaefer, B

    Rabinowitz, D. L., Schaefer, B. E., & Tourtellotte, S. W. 2007, AJ, 133, 26, doi: 10.1086/508931

    2007 doi

  80. [88]

    2023, matthewholman/assist: v1.1.1, v1.1.1, Zenodo, doi: 10.5281/zenodo.7778017

    Rein, H., Holman, M., & Akmal, A. 2023, matthewholman/assist: v1.1.1, v1.1.1, Zenodo, doi: 10.5281/zenodo.7778017

    2023 doi

  81. [89]

    Rein, H., & Liu, S. F. 2012, A&A, 537, A128, doi: 10.1051/0004-6361/201118085

    2012 doi

  82. [90]

    Rein, H., & Spiegel, D. S. 2015, MNRAS, 446, 1424, doi: 10.1093/mnras/stu2164

    2015 doi

  83. [91]

    E., Fitzsimmons, A., Young, D

    Robinson, J. E., Fitzsimmons, A., Young, D. R., et al. 2024, MNRAS, 531, 304, doi: 10.1093/mnras/stae966

    2024 doi

  84. [92]

    M., Poulet, F., & Sergeev, A

    Rousselot, P., Petit, J. M., Poulet, F., & Sergeev, A. 2005, Icarus, 176, 478, doi: 10.1016/j.icarus.2005.03.001

    2005 doi

  85. [93]

    K., et al

    Sarid, G., Volk, K., Steckloff, J. K., et al. 2019, ApJL, 883, L25, doi: 10.3847/2041-8213/ab3fb3

    2019 doi

  86. [94]

    E., Rabinowitz, D

    Schaefer, B. E., Rabinowitz, D. L., & Tourtellotte, S. W. 2009, AJ, 137, 129, doi: 10.1088/0004-6256/137/1/129

    2009 doi

  87. [95]

    E., Jones, R

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

    2018 doi arXiv

  88. [96]

    E., Fraser, W

    Schwamb, M. E., Fraser, W. C., Bannister, M. T., et al. 2019, ApJS, 243, 12, doi: 10.3847/1538-4365/ab2194

    2019 doi

  89. [97]

    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, doi: 10.3847/2515-5172/ac090f

    2021 doi

  90. [98]

    E., Jones, R

    Schwamb, M. E., Jones, R. L., Yoachim, P., et al. 2023, ApJS, 266, 22, doi: 10.3847/1538-4365/acc173 SCOC. 2022, Survey Cadence Optimization Committee’s Phase 1 Recommendations, Cadence Note PSTN-056, https://pstn-053.lsst.io/ —. 2023, Survey Cadence Optimization Committee’s P...

    2023 doi

  91. [99]

    2019, AJ, 157, 88, doi: 10.3847/1538-3881/aafbe4

    Fitzsimmons, A., & Cupani, G. 2019, AJ, 157, 88, doi: 10.3847/1538-3881/aafbe4

    2019 doi

  92. [100]

    2021, PSJ, 2, 234, doi: 10.3847/PSJ/ac2dee

    Jedicke, R. 2021, PSJ, 2, 234, doi: 10.3847/PSJ/ac2dee

    2021 doi

  93. [101]

    J., & Petit, J

    Kavelaars, J. J., & Petit, J. M. 2013, ApJL, 764, L2, doi: 10.1088/2041-8205/764/1/L2

    2013 doi

  94. [102]

    J., et al

    Shankman, C., Kavelaars, J., Gladman, B. J., et al. 2016, AJ, 151, 31, doi: 10.3847/0004-6256/151/2/31 30Murtagh et al

    2016 doi

  95. [103]

    P., Veras, D., & Wyatt, M

    Shannon, A., Jackson, A. P., Veras, D., & Wyatt, M. 2015, MNRAS, 446, 2059, doi: 10.1093/mnras/stu2267

    2015 doi

  96. [104]

    Brown, M. J. I., & Ashley, M. C. B. 2000, AJ, 120, 2687, doi: 10.1086/316805

    2000 doi

  97. [105]

    S., Trujillo, C., & Tholen, D

    Sheppard, S. S., Trujillo, C., & Tholen, D. J. 2016, ApJL, 825, L13, doi: 10.3847/2041-8205/825/1/L13

    2016 doi

  98. [106]

    S., Udalski, A., Trujillo, C., et al

    Sheppard, S. S., Udalski, A., Trujillo, C., et al. 2011, AJ, 142, 98, doi: 10.1088/0004-6256/142/4/98

    2011 doi

  99. [107]

    2016, AJ, 152, 103, doi: 10.3847/0004-6256/152/4/103

    Silsbee, K., & Tremaine, S. 2016, AJ, 152, 103, doi: 10.3847/0004-6256/152/4/103

    2016 doi

  100. [108]

    A., et al

    Solontoi, M., Ivezi´ c,ˇZ., West, A. A., et al. 2010, Icarus, 205, 605, doi: 10.1016/j.icarus.2009.07.042

    2010 doi

  101. [109]

    K., Sarid, G., Volk, K., et al

    Steckloff, J. K., Sarid, G., Volk, K., et al. 2020, ApJL, 904, L20, doi: 10.3847/2041-8213/abc888

    2020 doi

  102. [110]

    2008, in The Solar System Beyond Neptune, ed

    Peixinho, N. 2008, in The Solar System Beyond Neptune, ed. M. A. Barucci, H. Boehnhardt, D. P. Cruikshank, A. Morbidelli, & R. Dotson, 105–114

    2008

  103. [111]

    C., & Romanishin, W

    Tegler, S. C., & Romanishin, W. 1998, Nature, 392, 49, doi: 10.1038/32108 —. 2000, Nature, 407, 979, doi: 10.1038/35039572 —. 2003, Icarus, 161, 181, doi: 10.1016/S0019-1035(02)00021-0

    1998 doi

  104. [112]

    C., Romanishin, W., Consolmagno, G

    Tegler, S. C., Romanishin, W., Consolmagno, G. J., & J., S. 2016, AJ, 152, 210, doi: 10.3847/0004-6256/152/6/210

    2016 doi

  105. [113]

    S., & Malhotra, R

    Tiscareno, M. S., & Malhotra, R. 2003, AJ, 126, 3122, doi: 10.1086/379554

    2003 doi

  106. [114]

    Trujillo, C. A. 2008, in The Solar System Beyond Neptune, ed. M. A. Barucci, H. Boehnhardt, D. P. Cruikshank, A. Morbidelli, & R. Dotson, 573–585

    2008

  107. [115]

    2020, Journal of Open Source Software, 5, 1943, doi: 10.21105/joss.01943 van der Velden, E

    Uieda, L., Soler, S., Rampin, R., et al. 2020, Journal of Open Source Software, 5, 1943, doi: 10.21105/joss.01943 van der Velden, E. 2020, The Journal of Open Source Software, 5, 2004, doi: 10.21105/joss.02004

    2020 doi

  108. [116]

    J., Helfenstein, P., & Buratti, B

    Verbiscer, A. J., Helfenstein, P., & Buratti, B. J. 2013, in Astrophysics and Space Science

    2013

  109. [117]

    356, Astrophysics and Space Science Library, ed

    Library, Vol. 356, Astrophysics and Space Science Library, ed. M. S. Gudipati & J. Castillo-Rogez, 47, doi: 10.1007/978-1-4614-3076-6 2 Vereˇ s, P., & Chesley, S. R. 2017, AJ, 154, 13, doi: 10.3847/1538-3881/aa73d0

    2017 doi

  110. [118]

    E., et al

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

    2020 doi

  111. [119]

    2008, ApJ, 687, 714, doi: 10.1086/591839

    Volk, K., & Malhotra, R. 2008, ApJ, 687, 714, doi: 10.1086/591839

    2008 doi

  112. [120]

    2024, Research Notes of the American Astronomical Society, 8, 36, doi: 10.3847/2515-5172/ad22d4

    Volk, K., & Van Laerhoven, C. 2024, Research Notes of the American Astronomical Society, 8, 36, doi: 10.3847/2515-5172/ad22d4

    2024 doi

  113. [121]

    Waskom, M. L. 2021, Journal of Open Source Software, 6, 3021, doi: 10.21105/joss.03021

    2021 doi

  114. [122]

    J., Lilly, E., Chastel, S., et al

    Weryk, R. J., Lilly, E., Chastel, S., et al. 2016, arXiv e-prints, arXiv:1607.04895, doi: 10.48550/arXiv.1607.04895 Wes McKinney. 2010, in Proceedings of the 9th Python in Science Conference, ed. St´ efan van der Walt & Jarrod Millman, 56 – 61, doi: 10.25080/Majora-92bf1922-00a

    2016 doi arXiv

  115. [123]

    Wong, I., & Brown, M. E. 2016, AJ, 152, 90, doi: 10.3847/0004-6256/152/4/90 —. 2017, AJ, 153, 145, doi: 10.3847/1538-3881/aa60c3

    2016 doi

  116. [124]

    Neilsen, J., et al

    Yoachim, P., Jones, L., Eric H. Neilsen, J., et al. 2024, lsst/rubin scheduler: v3.4.0, v3.4.0, Zenodo, doi: 10.5281/zenodo.14232232

    2024 doi

  117. [125]

    Z., et al

    Yoachim, P., Coughlin, M., Angeli, G. Z., et al. 2016, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference

    2016

  118. [126]

    Peck, R. L. Seaman, & C. R. Benn, 99101A, doi: 10.1117/12.2232947

    doi

  119. [127]

    Neilsen, J., et al

    Yoachim, P., Jones, L., Eric H. Neilsen, J., et al. 2023, lsst/rubin sim: v2.0.0, v2.0.0, Zenodo, doi: 10.5281/zenodo.10215451

    2023 doi

  120. [128]

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

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

    2019 doi

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.