Pith. sign in

REVIEW 1 major objections 4 minor 1 cited by

This paper measures the light, stellar mass, and velocity dispersions of cluster members and the BCG/ICL in Abell S1063, producing the constraint dataset needed to separate dark matter from baryonic mass in a multi-probe cluster mass model.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

A new crowded-field light-profile fitting method yields light, kinematics, and stellar mass constraints for 289 cluster members plus the BCG and intra-cluster light in Abell S1063, feeding a future dark matter/baryon separation model.

T0 review reviewed 2026-08-04 challenge →

load-bearing objection A solid measurement release for AS1063 with a genuinely useful crowded-field fitting method; the outer Vrms bin rejection is the one spot that needs better defense. the 1 major comments →

arxiv 2509.07762 v2 pith:ZUDJPEDO submitted 2025-09-09 astro-ph.GA astro-ph.CO

A comprehensive separation of dark matter and baryonic mass components in galaxy clusters I: Mass constraints from Abell S1063

Benjamin Beauchesne , Benjamin Cl\'ement , Marceau Limousin , Bel\'en Alcalde Pampliega , Mathilde Jauzac , Anna Niemiec , Johan Richard , Guillaume Mahler
show 6 more authors
Jose M. Diego Pascale Hibon Anton M. Koekemoer Thomas Connor Jean-Paul Kneib Andreas L. Faisst
This is my paper
classification astro-ph.GA astro-ph.CO
keywords galaxies: clusters: generalgalaxies: clusters: individual: Abell S1063Galaxy: kinematics and dynamicsGalaxy: stellar contentdark matterintra-cluster lightmulti-probe mass modellingspectral energy distribution fitting
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

Galaxy clusters are mostly dark matter, but the baryons—hot gas and stars—can mimic or mask dark-matter signals unless they are modelled explicitly. This paper is the first half of a two-part series whose goal is a cluster mass model that keeps dark matter, intra-cluster gas, and stars as separate, individually constrained components. It presents the observational groundwork for that model in Abell S1063: light profiles for 289 cluster members, line-of-sight velocity dispersions at their half-light radii, a ten-point radial profile of the BCG and intra-cluster light velocity dispersion, and stellar masses from three independent spectral-energy-distribution models. To extract member light profiles in a crowded field, the paper develops a region-fitting and Bayesian refinement procedure that accounts for neighbours and contaminating objects. The measurements and intermediate data products are released, so the companion paper can use them to build the full baryon-dark matter decomposition.

Core claim

On its own terms, this paper claims to deliver the mass-constraint datasets needed to separate dark matter from baryonic mass in Abell S1063. Using dPIE profiles fitted to HST F160W imaging, it recovers light profiles for 289 spectroscopically confirmed cluster members; the brightest cluster galaxy and the intra-cluster light are modelled together as a single multi-Gaussian expansion. From MUSE integral-field spectroscopy it measures member line-of-sight velocity dispersions at their half-light radii and a twelve-point, reduced to ten-point, Vrms profile for the BCG/ICL in elliptical annuli. Stellar masses are estimated with three SED models, bracketing systematic differences. The paper's ce

What carries the argument

Two parametrisations carry the argument. For cluster members the paper uses the dual Pseudo-Isothermal Elliptical (dPIE) profile, chosen because its lensing quantities are analytic, so the fitted light profiles can be inserted directly into the lensing mass model that scales them to stellar mass. For the BCG and ICL it uses the Multi-Gaussian Expansion (MGE), the standard input for axisymmetric Jeans (JAM) kinematic modelling, enabling the Vrms profile to be turned into mass constraints. The supporting mechanism is a two-stage crowded-field fitting procedure: regions of overlapping galaxies are fitted simultaneously, then each galaxy is re-fitted individually with Bayesian sampling after sub

Load-bearing premise

The paper's mass constraints assume the two outermost BCG/ICL velocity measurements are dominated by systematic error rather than by a real rise in velocity dispersion; if those points are astrophysical, the Vrms profile steepens at large radius and the inferred total mass changes.

What would settle it

Re-extract the BCG/ICL spectra in the two outer annuli from the public MUSE cube with an alternative background subtraction or wider annuli. If the measured sigma_e values remain near 394 and 574 km/s (rather than dropping to the ~465 km/s trend of the inner ten points), the rejection is unjustified, and mass models that include those points will yield a steeper total mass profile beyond ~90 kpc.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • The released catalogue lets the companion model assign separate stellar and dark matter components to each of the 289 members, rather than treating them as a single mass sheet.
  • The BCG/ICL Vrms profile adds a kinematic probe in the cluster centre, where strong lensing has no image constraints, so the inner mass distribution can be tested.
  • Modelling the BCG/ICL MGE with several mass-to-light components allows a radially varying stellar mass-to-light ratio, connecting colour/metallicity gradients to the mass model.
  • The light-profile fitting method transfers to other HST-imaged clusters, providing homogeneous stellar mass constraints for cluster samples.
  • The three SED models bracket the stellar mass systematic, so the final dark matter profile can be quoted with a stellar population uncertainty.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The two outermost Vrms points (sigma_e = 394 and 574 km/s) were rejected as systematic noise without a quantitative criterion; if they are real, the Vrms profile rises steeply beyond ~90 kpc, which would imply a higher total mass at those radii than the paper's adopted profile suggests.
  • The same public data could be re-analysed with a different background treatment for the BCG/ICL spectra; if the high outer sigma_e values persist, the rejection should be reconsidered.
  • The Vrms values rise from ~341 km/s in the BCG to ~465 km/s in the ICL; this gradient, if extended to larger radii with deeper data, could test whether the ICL traces the cluster potential or a distinct dynamical component.
  • The paper's finding that MUSE-inclusive SED fits give higher stellar masses for the BCG/ICL than photometry-only fits (opposite to the cluster-member trend) suggests that including spectra can systematically shift stellar masses, a point worth propagating into the mass model's uncertainty budget.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 4 minor

Summary. The paper presents the data-acquisition and measurement pipeline for a two-paper programme aimed at separating dark matter from baryonic components in galaxy clusters, applied to Abell S1063. It develops a GPU-accelerated crowded-field light-profile fitting method using dPIE profiles for 289 cluster members and MGE models for the BCG+ICL, measures line-of-sight velocity dispersions of cluster members at their half-light radii, derives a 10-point Vrms profile for the BCG+ICL in elliptical annuli, and estimates stellar masses with three SED codes (two Bagpipes settings and LePhare). The measurements are released publicly and are intended to feed the joint lensing/X-ray/JAM mass model in the companion paper B25b.

Significance. If the measurements are correct, this is a useful and fairly comprehensive data paper: it combines new light-profile fitting in a crowded cluster field, kinematic extraction from MUSE, and SED-based stellar masses, with explicit residual diagnostics, mock-based calibration of the velocity-dispersion fitting, and public data release. The methodological care—three SED models, masking of neighbouring galaxies, Bayesian profile fitting, and release of intermediate products—is a genuine strength. The central risk is that the outermost BCG/ICL Vrms bins, which are the most relevant to the total-mass constraint at large radii, are rejected by an undocumented judgement call; a related inconsistency in the reported BCG/ICL stellar masses also needs attention. The paper is best seen as a methods/data paper whose full value will be demonstrated in B25b.

major comments (1)
  1. [§4.2] For cluster members, the σ_e errors are not rescaled for systematics because the scatter is added in the scaling relations. This is a reasonable choice, but the text says 'On average, this scatter is one order of magnitude larger than the total error of σ_e.' If that is the case, the individual σ_e errors have only a minor role in the scaling relations; please state this explicitly so that the reader can judge the impact of the 1.33 factor when the same pipeline is used for the BCG/ICL Vrms.
minor comments (4)
  1. [§5.2 / Fig. 9] Add markers or shading to distinguish the two failed annuli from the two rejected annuli; the phrase 'last four rejected data points' is ambiguous.
  2. [Appendix C] The 'axis ratio' bound is printed as U(1,20); for a physical axis ratio this range is inverted or the parameter is an inverse axis ratio. Please define and correct.
  3. [Throughout] Minor typographical and formatting issues: 'Morphofit' is used inconsistently; 'Figure2.Diagram' lacks a space; 'HSTimaging' in the abstract; several instances of missing spaces before references.
  4. [Table 1] The prior listed as 'Calibration: log10 σ_e (2.30,2.84)' is likely a range for the calibration of the spectral resolution, not a prior on the astrophysical velocity dispersion; please clarify in the table or text.

Circularity Check

0 steps flagged

No circular derivation: measurements are anchored to external data and standard libraries; the Vrms bin rejection is a data-quality judgement, not a circular step.

full rationale

This is a data paper that presents measurements — light profiles, stellar kinematics, and stellar masses — for cluster members and the BCG/ICL. The measurements are anchored to external HST, MUSE, and Chandra data, and to standard SED libraries and fitting codes (SExtractor, pPXF, UltraNest, JAX, etc.). The light-profile fits are validated against residuals rather than against any mass model, and stellar masses are derived with three independent SED models. The only self-referential elements are the Vrms error rescaling factor (1.33) and the LOSVD polynomial correction, both taken from the authors' own prior mock-calibration papers (B24, Beauchesne et al. 2025); these are calibrations derived from 10,000 mock spectra, not fits to the present data, so they do not make the measurements circular. The rejection of the two outermost Vrms bins in Sect. 5.2 ('the last two points do not follow the same pattern... thus we reject these last two data points as they appear to be dominated by systematic error') is a potentially debatable data-quality cut, and the paper itself notes ambiguity about 'the last four rejected data points,' but this is a selection/robustness concern, not a circular derivation: the accepted profile is not used to predict the rejected points. The companion paper B25b will use these measurements, but no mass-model result is derived here. No uniqueness theorem or ansatz is smuggled in via self-citation, and no fitted parameter is renamed as a prediction. Overall, the derivation chain is self-contained with respect to the data products presented; minor self-calibration warrants a low non-zero score.

Axiom & Free-Parameter Ledger

4 free parameters · 6 axioms · 0 invented entities

This paper is a measurement release: most fitted quantities are the output, not inputs. The real burdens are modelling assumptions (profile families, SED priors, background subtraction) and a set of ad hoc calibration constants (error rescaling, photometric systematic) inherited from the authors' own previous work. No new physical entities are introduced.

free parameters (4)
  • Vrms error rescaling factor = 1.33
    Multiplied into the BCG/ICL Vrms uncertainties (Sect. 5.2). Calibrated on 10,000 mock MUSE spectra in the authors' Beauchesne et al. (2025) paper; an in-house correction, not independently verified here.
  • Photometric homogenisation systematic = 5%
    Added to the BCG/ICL MGE photometry before SED fitting to account for different HST band depths (Sect. 5.1).
  • Bagpipes SED priors = ranges in Table 1
    Uniform or truncated-normal priors on log stellar mass formed (1-15), metallicity (0-2.5), V-band attenuation (0-2), age, and SFH parameters; stellar mass estimates depend on these choices.
  • Annulus construction choices = S/N > 5; R < 100 kpc; width = MUSE PSF
    These choices determine the number and width of the radial bins in the BCG/ICL Vrms profile (Sect. 5.2).
axioms (6)
  • domain assumption Cluster member light is well represented by dPIE profiles and field galaxy light by Sersic profiles in F160W
    Used throughout Sect. 4.1; the paper's own residuals show complex morphologies not captured by a single profile (Fig. 3).
  • domain assumption The BCG and ICL light is a single MGE component (normal or twisted) within the fitting aperture
    Sect. 5.1; required for the mass model and for JAM kinematic modelling in the companion paper.
  • domain assumption SED-derived stellar masses with Chabrier/Kroupa IMFs and the adopted SFH priors are unbiased enough for mass modelling
    Sects. 4.3 and 5.3; three models are used to bracket the systematic, but there is no external calibration against, e.g., resolved stellar masses.
  • domain assumption Measured LOSVD and Vrms can be interpreted as tracers of the gravitational potential via Jeans modelling using the fitted light profile
    This is the design of B25b; the current paper only reports the measurements, so the interpretation is not tested here.
  • domain assumption The ICL is not removed as background in the BCG/ICL kinematics extraction, while member galaxy spectra have local background subtracted
    Sect. 5.2 states MPDAF misidentifies ICL as sky, so local background is not subtracted for the ICL; for cluster members it is subtracted, possibly biasing faint members.
  • domain assumption Flat LambdaCDM with Omega_m=0.3, Omega_L=0.7, H0=70 km/s/Mpc
    Stated in the introduction; used for physical scale and mass conversions.

reviewed 2026-08-04 · how reviews work

0 comments
Cite this review

Pith. "Pith review of A comprehensive separation of dark matter and baryonic mass components in galaxy clusters I: Mass constraints from Abell S1063." pith.science (2026). https://pith.science/paper/ZUDJPEDO

@misc{pith2026250907762,
  author       = {Pith},
  title        = {Pith review of: A comprehensive separation of dark matter and baryonic mass components in galaxy clusters I: Mass constraints from Abell S1063},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZUDJPEDO}},
  note         = {Machine review of arXiv:2509.07762}
}
Share X Bluesky LinkedIn Reddit HN
abstract

In this two-part series, we present a multi-probe mass modelling method for massive galaxy clusters, designed to disentangle the contributions of individual mass components (Dark matter, intra-cluster gas, stellar masses). In this first paper, we focus on recovering the mass constraint datasets required for the modelling approach introduced in the second paper. Specifically, we measure the light distribution, stellar mass, and kinematics of the cluster members, the brightest cluster galaxy (BCG), and the intra-cluster light (ICL) in Abell S1063. To that end, we developed a new method to extract the light profiles of the cluster members, BCG, and ICL, while accounting for contamination from nearby foreground and background galaxies in \textsc{Hubble Space Telescope} (HST) imaging. We obtained light profiles for $289$ cluster members using a dual Pseudo-Isothermal Elliptical (dPIE) model based on the HST F160W filter, while the BCG \& ICL is modelled as a single component using a multi-Gaussian expansion. To estimate stellar masses and velocity dispersions, we rely on multi-band HST photometry and \textsc{VLT/MUSE} integral field spectroscopy, respectively. Stellar masses are derived using three different spectral energy distribution (SED) models. We measure the line-of-sight velocity dispersions of the cluster members at their half-light radii, as determined from their light profiles, while for the BCG \& ICL components, we use elliptical annular apertures. Thanks to these measurements, we will be able to constrain the cluster stellar mass content, which is detailed in the second paper of the series. We publicly release these measurements with intermediary data products.

Figures

Figures reproduced from arXiv: 2509.07762 by Andreas L. Faisst, Anna Niemiec, Anton M. Koekemoer, Bel\'en Alcalde Pampliega, Benjamin Beauchesne, Benjamin Cl\'ement, Guillaume Mahler, Jean-Paul Kneib, Johan Richard, Jose M. Diego, Marceau Limousin, Mathilde Jauzac, Pascale Hibon, Thomas Connor.

Figure 1
Figure 1. Figure 1: BUFFALO composite colour image of AS1063 with the following HST filters: F435W (blue), F606W (green) and F814W (red). The footprint of the combined MUSE observations is highlighted by the white dashed rectangle. The X-ray surface brightness from the Chandra X-ray Observatory is shown by the green dashed contours. The set of multiple images used in this work is highlighted by the cyan dots. Red and blue dot… view at source ↗
Figure 2
Figure 2. Figure 2: Diagram of the workflow to fit the cluster members light distribution. The first step is split between estimating the background and building the galaxy catalogue. We extract the necessary quantities from this step to proceed to the first fitting step: PSF estimation, masking, parameter bounds, and preliminary ICL estimation. The second step is based on fitting regions that include multiple galaxies. Regio… view at source ↗
Figure 3
Figure 3. Figure 3: Region of 180” × 180” around the centre of AS1063: Original (top), model (middle) and residual (bottom) from the galaxy fitting procedure considering the F160W filter. MNRAS 000, 1–18 (2025) [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of the weighted residuals of the galaxy fit procedure for the filter F160W. This distribution is extracted from the masked area where galaxy fits are performed, and is presented with the cyan plain line. Plain red and blue lines represent the best-fitting results of a Laplace and Gaussian distribution. They highlight the broader tails of our residual distribution com￾pared to a Gaussian distri… view at source ↗
Figure 5
Figure 5. Figure 5: Cluster member stellar masses (𝑀∗) as a function of of their apparent magnitude in the F160W filter (𝑚F160W). Error bars on 𝑀∗ present the 1𝜎 credible interval from the SED posterior parameters while representing the standard deviation for F160W. The magenta, cyan, and lime coloured dots represent the stellar mass estimates provided by Bagpipes with a double power-law SFH, Bagpipes with a delayed SFH, and … view at source ↗
Figure 6
Figure 6. Figure 6: Diagram of the workflow to extract the BCG and the ICL kinematics, and their light distributions. The light distribution is estimated in the data image, where light profiles of galaxies have been removed. We made this estimation with MGE fits, using two parametrisations: an axisymmetric and a concentric. We define the extraction regions for the kinematics fit and estimate the lensing quantities based on th… view at source ↗
Figure 8
Figure 8. Figure 8: best-fitting MGE model with an axisymmetric parametrisation (top) and associated residuals (bottom). F160W filter. When all SED models and data points are combined, we obtain Υ BCG, F160W ∗ = 8.57 ± 1.42 𝑀⊙/𝐿⊙,𝑅 in comparison to Υ BCG, F814W ∗ = 11.57±1.90 𝑀⊙/𝐿⊙,𝑅. Hence, there is ≈ 30 per cent more dispersion in the F814W than in F160W filters, which favours the use of light distribution in F160W as a tra… view at source ↗
Figure 7
Figure 7. Figure 7: From top to bottom: Masked HST image in the F160W filter where all galaxy models have been subtracted, best-fitting MGE model on twisted isophotes and associated residuals account for roughly half of the stellar mass of the BCG and the ICL components. Regarding a possible gradient in Υ BCG ∗ , the radial variation is limited in both F814W and F160W filters. The variation between the data points is mostly w… view at source ↗
Figure 10
Figure 10. Figure 10: Top panel: Stellar-mass-to-light-ratio Υ BCG ∗ of the BCG and the ICL components in the F814W filter. The luminosity has been normalised by the solar luminosity in the R-band from Willmer (2018). Error bars represent standard deviations on Υ BCG ∗ made by propagating the error on the stellar mass and photometry, while the radius error represents the width of the extraction regions. To improve the readabil… view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. SLICE -- Combining Strong Lensing and X-ray in AC 114. Further Insights into the Merger Scenario

    astro-ph.CO 2025-12 unverdicted novelty 6.0

    Combined JWST lensing and X-ray analysis shows AC114 as the main cluster in a late post-collisional major merger with a gas-stripped companion AC114b located about 1 Mpc to the northwest.

Reference graph

Works this paper leans on

96 extracted references · 10 canonical work pages · cited by 1 Pith paper · 1 internal anchor

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

  3. [3]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  4. [4]

    O., Corwin Harold G

    Abell G. O., Corwin Harold G. J., Olowin R. P., 1989, @doi [ ] 10.1086/191333 , https://ui.adsabs.harvard.edu/abs/1989ApJS...70....1A 70, 1

    doi:10.1086/191333 1989

  5. [5]

    P., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21918.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427..948A 427, 948

    Amara A., Quanz S. P., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21918.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427..948A 427, 948

    arXiv 2012

  6. [6]

    E., Fuson J., Gad-Nasr S., Kong D., Minor Q., Roberts M

    Andrade K. E., Fuson J., Gad-Nasr S., Kong D., Minor Q., Roberts M. G., Kaplinghat M., 2022, @doi [ ] 10.1093/mnras/stab3241 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.510...54A 510, 54

    doi:10.1093/mnras/stab3241 2022

  7. [7]

    Arnouts S., et al., 2013, @doi [ ] 10.1051/0004-6361/201321768 , https://ui.adsabs.harvard.edu/abs/2013A&A...558A..67A 558, A67

    doi:10.1051/0004-6361/201321768 2013

  8. [8]

    S., Ramsay S

    Bacon R., et al., 2010, in McLean I. S., Ramsay S. K., Takami H., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 7735, Ground-based and Airborne Instrumentation for Astronomy III. p. 773508 ( @eprint arXiv 2211.16795 ), @doi 10.1117/12.856027

    Pith/arXiv arXiv 2010

  9. [9]

    Bacon R., Piqueras L., Conseil S., Richard J., Shepherd M., 2016, MPDAF: MUSE Python Data Analysis Framework , Astrophysics Source Code Library, record ascl:1611.003

    2016

  10. [10]

    Beauchesne B., 2025, A comprehensive separation of dark matter and baryonic mass components in galaxy clusters II: an overview of the mass distribution in Abell S1063

    2025

  11. [11]

    Beauchesne B., et al., 2024, @doi [ ] 10.1093/mnras/stad3308 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.3246B 527, 3246

    doi:10.1093/mnras/stad3308 2024

  12. [12]

    Beauchesne B., et al., 2025, @doi [ ] 10.1093/mnras/stae2695 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.536.2086B 536, 2086

    work page doi:10.1093/mnras/stae2695 2025

  13. [13]

    Bergamini P., et al., 2019, @doi [ ] 10.1051/0004-6361/201935974 , https://ui.adsabs.harvard.edu/abs/2019A&A...631A.130B 631, A130

    doi:10.1051/0004-6361/201935974 2019

  14. [14]

    Bertin E., Arnouts S., 1996, @doi [ ] 10.1051/aas:1996164 , https://ui.adsabs.harvard.edu/abs/1996A&AS..117..393B 117, 393

    doi:10.1051/aas:1996164 1996

  15. [15]

    R., Mol J

    Bertin E., Schefer M., Apostolakos N., \'A lvarez-Ayll \'o n A., Dubath P., K \"u mmel M., 2020, in Pizzo R., Deul E. R., Mol J. D., de Plaa J., Verkouter H., eds, Astronomical Society of the Pacific Conference Series Vol. 527, Astronomical Data Analysis Software and Systems XXIX. p. 461

    2020

  16. [16]

    Bonamigo M., et al., 2017, @doi [ ] 10.3847/1538-4357/aa75cc , https://ui.adsabs.harvard.edu/abs/2017ApJ...842..132B 842, 132

    work page doi:10.3847/1538-4357/aa75cc 2017

  17. [17]

    Bonamigo M., et al., 2018, @doi [ ] 10.3847/1538-4357/aad4a7 , https://ui.adsabs.harvard.edu/abs/2018ApJ...864...98B 864, 98

    work page doi:10.3847/1538-4357/aad4a7 2018

  18. [18]

    W., Treu T., Ebeling H., Massey R., Morris R

    Brada c M., Allen S. W., Treu T., Ebeling H., Massey R., Morris R. G., von der Linden A., Applegate D., 2008, @doi [ ] 10.1086/591246 , https://ui.adsabs.harvard.edu/abs/2008ApJ...687..959B 687, 959

    doi:10.1086/591246 2008

  19. [19]

    Bradbury J., et al., 2021, JAX: Autograd and XLA , Astrophysics Source Code Library, record ascl:2111.002

    2021

  20. [20]

    R., Moffat J

    Brownstein J. R., Moffat J. W., 2007, @doi [ ] 10.1111/j.1365-2966.2007.12275.x , https://ui.adsabs.harvard.edu/abs/2007MNRAS.382...29B 382, 29

    arXiv 2007

  21. [22]

    Buchner J., 2019, @doi [ ] 10.1088/1538-3873/aae7fc , https://ui.adsabs.harvard.edu/abs/2019PASP..131j8005B 131, 108005

    doi:10.1088/1538-3873/aae7fc 2019

  22. [23]

    Buchner J., 2021, @doi [The Journal of Open Source Software] 10.21105/joss.03001 , https://ui.adsabs.harvard.edu/abs/2021JOSS....6.3001B 6, 3001

    doi:10.21105/joss.03001 2021

  23. [25]

    Buchner J., et al., 2014b, @doi [ ] 10.1051/0004-6361/201322971 , https://ui.adsabs.harvard.edu/abs/2014A&A...564A.125B 564, A125

    doi:10.1051/0004-6361/201322971

  24. [26]

    C., Kinney A

    Calzetti D., Armus L., Bohlin R. C., Kinney A. L., Koornneef J., Storchi-Bergmann T., 2000, @doi [ ] 10.1086/308692 , https://ui.adsabs.harvard.edu/abs/2000ApJ...533..682C 533, 682

    doi:10.1086/308692 2000

  25. [27]

    B., et al., 2016, @doi [ ] 10.1051/0004-6361/201527670 , https://ui.adsabs.harvard.edu/abs/2016A&A...587A..80C 587, A80

    Caminha G. B., et al., 2016, @doi [ ] 10.1051/0004-6361/201527670 , https://ui.adsabs.harvard.edu/abs/2016A&A...587A..80C 587, A80

    doi:10.1051/0004-6361/201527670 2016

  26. [28]

    Cappellari M., 2002, @doi [ ] 10.1046/j.1365-8711.2002.05412.x , https://ui.adsabs.harvard.edu/abs/2002MNRAS.333..400C 333, 400

    arXiv 2002

  27. [29]

    Cappellari M., 2008, @doi [ ] 10.1111/j.1365-2966.2008.13754.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.390...71C 390, 71

    arXiv 2008

  28. [30]

    Cappellari M., 2017, @doi [ ] 10.1093/mnras/stw3020 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.466..798C 466, 798

    doi:10.1093/mnras/stw3020 2017

  29. [31]

    Cappellari M., Copin Y., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06541.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.342..345C 342, 345

    arXiv 2003

  30. [32]

    C., McLure R

    Carnall A. C., McLure R. J., Dunlop J. S., Dav \'e R., 2018, @doi [ ] 10.1093/mnras/sty2169 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.4379C 480, 4379

    doi:10.1093/mnras/sty2169 2018

  31. [33]

    C., Leja J., Johnson B

    Carnall A. C., Leja J., Johnson B. D., McLure R. J., Dunlop J. S., Conroy C., 2019, @doi [ ] 10.3847/1538-4357/ab04a2 , https://ui.adsabs.harvard.edu/abs/2019ApJ...873...44C 873, 44

    doi:10.3847/1538-4357/ab04a2 2019

  32. [34]

    C., et al., 2023, @doi [ ] 10.1038/s41586-023-06158-6 , https://ui.adsabs.harvard.edu/abs/2023Natur.619..716C 619, 716

    Carnall A. C., et al., 2023, @doi [ ] 10.1038/s41586-023-06158-6 , https://ui.adsabs.harvard.edu/abs/2023Natur.619..716C 619, 716

    doi:10.1038/s41586-023-06158-6 2023

  33. [35]

    arXiv:2506.21531

    Cerny C., Jauzac M., Lagattuta D., Niemiec A., Mahler G., Edge A., Massey R., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2506.21531 , https://ui.adsabs.harvard.edu/abs/2025arXiv250621531C p. arXiv:2506.21531

    Pith/arXiv arXiv doi:10.48550/arxiv.2506.21531 2025

  34. [36]

    Chabrier G., 2003, @doi [ ] 10.1086/376392 , https://ui.adsabs.harvard.edu/abs/2003PASP..115..763C 115, 763

    doi:10.1086/376392 2003

  35. [37]

    Clowe D., Gonzalez A., Markevitch M., 2004, @doi [ ] 10.1086/381970 , https://ui.adsabs.harvard.edu/abs/2004ApJ...604..596C 604, 596

    doi:10.1086/381970 2004

  36. [38]

    H., Markevitch M., Randall S

    Clowe D., Brada c M., Gonzalez A. H., Markevitch M., Randall S. W., Jones C., Zaritsky D., 2006, @doi [ ] 10.1086/508162 , https://ui.adsabs.harvard.edu/abs/2006ApJ...648L.109C 648, L109

    doi:10.1086/508162 2006

  37. [39]

    E., et al., 2018, @doi [Science] 10.1126/science.aao2469 , https://ui.adsabs.harvard.edu/abs/2018Sci...360.1342C 360, 1342

    Collett T. E., et al., 2018, @doi [Science] 10.1126/science.aao2469 , https://ui.adsabs.harvard.edu/abs/2018Sci...360.1342C 360, 1342

    doi:10.1126/science.aao2469 2018

  38. [40]

    Conroy C., 2013, @doi [ ] 10.1146/annurev-astro-082812-141017 , https://ui.adsabs.harvard.edu/abs/2013ARA&A..51..393C 51, 393

    doi:10.1146/annurev-astro-082812-141017 2013

  39. [41]

    Da Rocha C., Mendes de Oliveira C., 2005, @doi [ ] 10.1111/j.1365-2966.2005.09641.x , https://ui.adsabs.harvard.edu/abs/2005MNRAS.364.1069D 364, 1069

    arXiv 2005

  40. [42]

    Dai D.-C., Matsuo R., Starkman G., 2008, @doi [ ] 10.1103/PhysRevD.78.104004 , https://ui.adsabs.harvard.edu/abs/2008PhRvD..78j4004D 78, 104004

    work page doi:10.1103/physrevd.78.104004 2008

  41. [43]

    M., Broadhurst T., Wong J., Silk J., Lim J., Zheng W., Lam D., Ford H., 2016, @doi [ ] 10.1093/mnras/stw865 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.459.3447D 459, 3447

    Diego J. M., Broadhurst T., Wong J., Silk J., Lim J., Zheng W., Lam D., Ford H., 2016, @doi [ ] 10.1093/mnras/stw865 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.459.3447D 459, 3447

    doi:10.1093/mnras/stw865 2016

  42. [44]

    G., 2022, @doi [ ] 10.1051/0004-6361/202243205 , https://ui.adsabs.harvard.edu/abs/2022A&A...666A..41E 666, A41

    Eckert D., Ettori S., Robertson A., Massey R., Pointecouteau E., Harvey D., McCarthy I. G., 2022, @doi [ ] 10.1051/0004-6361/202243205 , https://ui.adsabs.harvard.edu/abs/2022A&A...666A..41E 666, A41

    doi:10.1051/0004-6361/202243205 2022

  43. [45]

    Ellien A., et al., 2021, @doi [ ] 10.1051/0004-6361/202038419 , https://ui.adsabs.harvard.edu/abs/2021A&A...649A..38E 649, A38

    doi:10.1051/0004-6361/202038419 2021

  44. [46]

    H., Giodini S., Lovisari L., Schmidt R

    Ettori S., Donnarumma A., Pointecouteau E., Reiprich T. H., Giodini S., Lovisari L., Schmidt R. W., 2013, @doi [ ] 10.1007/s11214-013-9976-7 , https://ui.adsabs.harvard.edu/abs/2013SSRv..177..119E 177, 119

    doi:10.1007/s11214-013-9976-7 2013

  45. [47]

    N., et al., 2010, @doi [ ] 10.1088/0067-0049/189/1/37 , https://ui.adsabs.harvard.edu/abs/2010ApJS..189...37E 189, 37

    Evans I. N., et al., 2010, @doi [ ] 10.1088/0067-0049/189/1/37 , https://ui.adsabs.harvard.edu/abs/2010ApJS..189...37E 189, 37

    doi:10.1088/0067-0049/189/1/37 2010

  46. [48]

    J., Gorgas J., Peletier R

    Falc \'o n-Barroso J., S \'a nchez-Bl \'a zquez P., Vazdekis A., Ricciardelli E., Cardiel N., Cenarro A. J., Gorgas J., Peletier R. F., 2011, @doi [ ] 10.1051/0004-6361/201116842 , https://ui.adsabs.harvard.edu/abs/2011A&A...532A..95F 532, A95

    doi:10.1051/0004-6361/201116842 2011

  47. [49]

    P., Cameron E., Pettitt A

    Feroz F., Hobson M. P., Cameron E., Pettitt A. N., 2019, @doi [The Open Journal of Astrophysics] 10.21105/astro.1306.2144 , https://ui.adsabs.harvard.edu/abs/2019OJAp....2E..10F 2, 10

    Pith/arXiv arXiv 2019

  48. [50]

    Giallongo E., et al., 2014, @doi [ ] 10.1088/0004-637X/781/1/24 , https://ui.adsabs.harvard.edu/abs/2014ApJ...781...24G 781, 24

    work page doi:10.1088/0004-637x/781/1/24 2014

  49. [51]

    Granata G., et al., 2022, @doi [ ] 10.1051/0004-6361/202141817 , https://ui.adsabs.harvard.edu/abs/2022A&A...659A..24G 659, A24

    doi:10.1051/0004-6361/202141817 2022

  50. [52]

    Harvey D., Massey R., Kitching T., Taylor A., Tittley E., 2015, @doi [Science] 10.1126/science.1261381 , https://ui.adsabs.harvard.edu/abs/2015Sci...347.1462H 347, 1462

    doi:10.1126/science.1261381 2015

  51. [53]

    Herbel J., Kacprzak T., Amara A., Refregier A., Lucchi A., 2018, @doi [ ] 10.1088/1475-7516/2018/07/054 , https://ui.adsabs.harvard.edu/abs/2018JCAP...07..054H 2018, 054

    doi:10.1088/1475-7516/2018/07/054 2018

  52. [54]

    Horne K., 1986, @doi [ ] 10.1086/131801 , https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H 98, 609

    doi:10.1086/131801 1986

  53. [55]

    Ilbert O., et al., 2015, @doi [ ] 10.1051/0004-6361/201425176 , https://ui.adsabs.harvard.edu/abs/2015A&A...579A...2I 579, A2

    doi:10.1051/0004-6361/201425176 2015

  54. [56]

    C., Harding P., Feldmeier J

    Janowiecki S., Mihos J. C., Harding P., Feldmeier J. J., Rudick C., Morrison H., 2010, @doi [ ] 10.1088/0004-637X/715/2/972 , https://ui.adsabs.harvard.edu/abs/2010ApJ...715..972J 715, 972

    doi:10.1088/0004-637x/715/2/972 2010

  55. [57]

    Jim \'e nez-Teja Y., Dupke R., 2016, @doi [ ] 10.3847/0004-637X/820/1/49 , https://ui.adsabs.harvard.edu/abs/2016ApJ...820...49J 820, 49

    doi:10.3847/0004-637x/820/1/49 2016

  56. [58]

    L., Sharon K., Bayliss M

    Johnson T. L., Sharon K., Bayliss M. B., Gladders M. D., Coe D., Ebeling H., 2014, @doi [ ] 10.1088/0004-637X/797/1/48 , https://ui.adsabs.harvard.edu/abs/2014ApJ...797...48J 797, 48

    doi:10.1088/0004-637x/797/1/48 2014

  57. [59]

    Kassiola A., Kovner I., 1993, @doi [ ] 10.1086/173325 , https://ui.adsabs.harvard.edu/abs/1993ApJ...417..450K 417, 450

    doi:10.1086/173325 1993

  58. [60]

    Kluge M., et al., 2020, @doi [ ] 10.3847/1538-4365/ab733b , https://ui.adsabs.harvard.edu/abs/2020ApJS..247...43K 247, 43

    doi:10.3847/1538-4365/ab733b 2020

  59. [61]

    Kneib J.-P., Natarajan P., 2011, @doi [ ] 10.1007/s00159-011-0047-3 , https://ui.adsabs.harvard.edu/abs/2011A&ARv..19...47K 19, 47

    doi:10.1007/s00159-011-0047-3 2011

  60. [62]

    M., et al., 2011, @doi [ ] 10.1088/0067-0049/197/2/36 , https://ui.adsabs.harvard.edu/abs/2011ApJS..197...36K 197, 36

    Koekemoer A. M., et al., 2011, @doi [ ] 10.1088/0067-0049/197/2/36 , https://ui.adsabs.harvard.edu/abs/2011ApJS..197...36K 197, 36

    doi:10.1088/0067-0049/197/2/36 2011

  61. [63]

    Kroupa P., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04022.x , https://ui.adsabs.harvard.edu/abs/2001MNRAS.322..231K 322, 231

    arXiv 2001

  62. [64]

    Limousin M., Kneib J.-P., Natarajan P., 2005, @doi [ ] 10.1111/j.1365-2966.2004.08449.x , https://ui.adsabs.harvard.edu/abs/2005MNRAS.356..309L 356, 309

    arXiv 2005

  63. [65]

    Limousin M., Beauchesne B., Jullo E., 2022, @doi [ ] 10.1051/0004-6361/202243278 , https://ui.adsabs.harvard.edu/abs/2022A&A...664A..90L 664, A90

    doi:10.1051/0004-6361/202243278 2022

  64. [66]

    Longobardi A., Arnaboldi M., Gerhard O., Pulsoni C., S \"o ldner-Rembold I., 2018, @doi [ ] 10.1051/0004-6361/201832729 , https://ui.adsabs.harvard.edu/abs/2018A&A...620A.111L 620, A111

    work page doi:10.1051/0004-6361/201832729 2018

  65. [67]

    M., et al., 2017, @doi [ ] 10.3847/1538-4357/837/1/97 , https://ui.adsabs.harvard.edu/abs/2017ApJ...837...97L 837, 97

    Lotz J. M., et al., 2017, @doi [ ] 10.3847/1538-4357/837/1/97 , https://ui.adsabs.harvard.edu/abs/2017ApJ...837...97L 837, 97

    doi:10.3847/1538-4357/837/1/97 2017

  66. [68]

    A., 2022, MAMPOSSt: Mass/orbit modeling of spherical systems , Astrophysics Source Code Library, record ascl:2203.020

    Mamon G. A., 2022, MAMPOSSt: Mass/orbit modeling of spherical systems , Astrophysics Source Code Library, record ascl:2203.020

    2022

  67. [69]

    A., Biviano A., Boué G., 2013, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/sts565 , 429, 3079–3098

    Mamon G. A., Biviano A., Boué G., 2013, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/sts565 , 429, 3079–3098

    doi:10.1093/mnras/sts565 2013

  68. [70]

    Meneghetti M., et al., 2020, @doi [Science] 10.1126/science.aax5164 , https://ui.adsabs.harvard.edu/abs/2020Sci...369.1347M 369, 1347

    doi:10.1126/science.aax5164 2020

  69. [71]

    Mercurio A., et al., 2021, @doi [ ] 10.1051/0004-6361/202142168 , https://ui.adsabs.harvard.edu/abs/2021A&A...656A.147M 656, A147

    doi:10.1051/0004-6361/202142168 2021

  70. [72]

    Cauchy Loss Function: Robustness Under Gaussian and Cauchy Noise

    Mlotshwa T., van Deventer H., Sergeevna Bosman A., 2023, @doi [arXiv e-prints] 10.48550/arXiv.2302.07238 , https://ui.adsabs.harvard.edu/abs/2023arXiv230207238M p. arXiv:2302.07238

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2302.07238 2023

  71. [73]

    Monna A., et al., 2014, @doi [ ] 10.1093/mnras/stt2284 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.438.1417M 438, 1417

    doi:10.1093/mnras/stt2284 2014

  72. [74]

    Montes M., 2022, @doi [Nature Astronomy] 10.1038/s41550-022-01616-z , https://ui.adsabs.harvard.edu/abs/2022NatAs...6..308M 6, 308

    doi:10.1038/s41550-022-01616-z 2022

  73. [75]

    Montes M., Trujillo I., 2014, @doi [ ] 10.1088/0004-637X/794/2/137 , https://ui.adsabs.harvard.edu/abs/2014ApJ...794..137M 794, 137

    doi:10.1088/0004-637x/794/2/137 2014

  74. [76]

    Montes M., Trujillo I., 2018, @doi [ ] 10.1093/mnras/stx2847 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474..917M 474, 917

    doi:10.1093/mnras/stx2847 2018

  75. [77]

    E., Treu T., Schmidt K

    Morishita T., Abramson L. E., Treu T., Schmidt K. B., Vulcani B., Wang X., 2017, @doi [ ] 10.3847/1538-4357/aa8403 , https://ui.adsabs.harvard.edu/abs/2017ApJ...846..139M 846, 139

    doi:10.3847/1538-4357/aa8403 2017

  76. [78]

    Natarajan P., et al., 2017, @doi [ ] 10.1093/mnras/stw3385 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.468.1962N 468, 1962

    doi:10.1093/mnras/stw3385 2017

  77. [79]

    Natarajan P., Williams L. L. R., Brada c M., Grillo C., Ghosh A., Sharon K., Wagner J., 2024, @doi [ ] 10.1007/s11214-024-01051-8 , https://ui.adsabs.harvard.edu/abs/2024SSRv..220...19N 220, 19

    doi:10.1007/s11214-024-01051-8 2024

  78. [80]

    B., Treu T., Ellis R

    Newman A. B., Treu T., Ellis R. S., Sand D. J., 2013, @doi [ ] 10.1088/0004-637X/765/1/25 , https://ui.adsabs.harvard.edu/abs/2013ApJ...765...25N 765, 25

    doi:10.1088/0004-637x/765/1/25 2013

  79. [81]

    Pedregosa F., et al., 2011, Journal of Machine Learning Research, 12, 2825

    2011

  80. [82]

    Postman M., et al., 2012, @doi [ ] 10.1088/0067-0049/199/2/25 , https://ui.adsabs.harvard.edu/abs/2012ApJS..199...25P 199, 25

    doi:10.1088/0067-0049/199/2/25 2012

Showing first 80 references.

This paper was first reviewed by deepseek-v4-flash on August 4, 2026.