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REVIEW 3 major objections 6 minor 203 references

The Last Stand Before Rubin: a consolidated sample of strong lensing systems in wide-field surveys

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper builds the LaStBeRu database of 31,569 unique strong-lensing systems with 366,905 image cutouts from seven wide-field surveys, and uses a 206-system ground-based subsample to measure $\gamma_{\mathrm{PPN}} = 1.023 \pm 0.028$…

desk verdict LaStBeRu is a genuinely valuable strong-lensing database with careful construction; the gamma_PPN result is a legitimate but prior-limited application whose quoted error needs a systematic term. read the letter →

arxiv 2509.09798 v1 pith:NLBQYOK2 submitted 2025-09-11 astro-ph.GA astro-ph.CO

Renan Alves de Oliveira , João Paulo C. França , Martín Makler This is my paper · ORCID
classification astro-ph.GAastro-ph.CO
keywords stronggravitationallensinggalaxy-galaxycatalogueswide-fieldsurveyspost-Newtonianparametertestsofgeneralrelativitystellarvelocitydispersionsground-basedimaging
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 assembles a single consolidated database, LaStBeRu, of 31,569 unique strong gravitational lensing systems drawn from the literature, enriched by cross-matching with photometric and spectroscopic surveys and accompanied by 366,905 image cutouts from seven wide-field ground-based surveys. The authors argue this is the most complete ground-based watch list of known lenses available before the next generation of wide-field surveys begins, useful for training automated finders, planning follow-up observations, and archival cosmological analyses. As a demonstration, they select 206 isolated early-type galaxy lenses that are clearly identifiable in ground-based images and use them, together with published samples, to constrain the post-Newtonian parameter that measures how much space curvature light feels compared with matter. Their MCMC analysis yields $\gamma_{\mathrm{PPN}} = 1.023 \pm 0.028$ for the new ground-based sample alone and $\gamma_{\mathrm{PPN}} = 1.034 \pm 0.025$ when combined with two earlier catalogs, which they describe as the tightest constraint of this kind to date; both results agree with general relativity at the quoted uncertainty level.

What carries the argument

The framework has two load-bearing parts. The aggregation pipeline ('slcomp') gives every system a unique coordinate-based JNAME, merges duplicates within a 10-arcsecond radius, cross-matches against photometric and spectroscopic catalogs at a 1-arcsecond radius, and applies hierarchical rules to assign one adopted value per physical quantity while keeping full provenance. The gravity test then rests on an analytic model that equates the luminosity-weighted, seeing-convolved line-of-sight velocity dispersion of an early-type galaxy to the value predicted from its Einstein radius and the lens-source distance ratio, assuming power-law mass and light profiles $\rho(r) \propto r^{-\alpha}$, $\nu(r) \propto r^{-\delta}$, and a constant velocity-anisotropy parameter $\beta$. In this model lensing responds to the sum of the two metric potentials $\Phi + \Psi$ while stellar dynamics respond to $\Phi$ alone, and the factor $2/(1+\gamma_{\mathrm{PPN}})$ in the predicted dispersion is what the data constrain.

What would settle it

Re-run the MCMC analysis with the same 206 systems but replace the global Gaussian priors on $\alpha$, $\beta$, $\delta$ with per-system values measured from independent data, such as spatially resolved IFU kinematic profiles for a subset of lenses. If the resulting $\gamma_{\mathrm{PPN}}$ moves outside the quoted $1.034 \pm 0.025$ by more than the statistical uncertainty, the prior-dominated assumption of a homologous population is doing the work. A simpler check: the authors' own aperture-corrected and uncorrected results differ by 0.044 (1.023 vs 0.979), which is larger than either statistical error bar, so an analysis that fully propagates uncertainties in effective radii and seeing would reveal whether that missing systematic explains the difference.

Watch

Extended reading notes

Core claim

The central claim has two parts. First, the LaStBeRu database consolidates 31,569 unique strong-lensing systems from many separate literature catalogs, resolves duplicate entries by averaging positions into a single coordinate-based identifier, cross-matches them with photometric and spectroscopic surveys to add magnitudes, redshifts, and velocity dispersions, and delivers 20-arcsecond and 4-arcminute image cutouts in every available band of seven ground-based surveys. Second, using a visually inspected subsample of 206 isolated early-type galaxy lenses that show clear lensing features in ground-based images and whose velocity dispersions come from a single spectroscopic survey, the paper obtains an independent measurement of the post-Newtonian parameter $\gamma_{\mathrm{PPN}} = 1.023 \pm 0.028$ by combining strong-lensing geometry with stellar dynamics. Combining this sample with two earlier published catalogs, for a total of 280 unique systems, yields $\gamma_{\mathrm{PPN}} = 1.034 \pm 0.025$, which the authors report as the tightest constraint from this kind of analysis to date and consistent with general relativity.

Load-bearing premise

The load-bearing premise is that all 206 lenses form one homologous population of early-type galaxies, so a single global power-law mass slope, light slope, and velocity-anisotropy parameter can be enforced through Gaussian priors centered at $\langle\alpha\rangle = 2.00$, $\langle\beta\rangle = 0.18$, and $\langle\delta\rangle = 2.40$. If the real lenses spread more widely in slope or anisotropy, the inferred $\gamma_{\mathrm{PPN}}$ shifts; the fact that the posteriors for $\beta$ and $\delta$ are prior-dominated means the priors, not the data, anchor those parameters.

Editorial extensions

If this is right

  • The LaStBeRu database can serve as a watch list for lensed transient events and as a benchmark for training machine-learning lens finders on ground-based data, directly addressing the expected flood of candidates from the next generation of wide-field surveys.
  • The 206-system ground-based sample shows that competitive $\gamma_{\mathrm{PPN}}$ constraints can be obtained without space-based imaging, using homogeneous ground-based images and a single spectroscopic survey's velocity dispersions.
  • Combining LaStBeRu with the two earlier catalogs produces the tightest $\gamma_{\mathrm{PPN}}$ constraint of this method so far, so future measurements of additional velocity dispersions can push the constraint further.
  • The database's cross-matching adds thousands of new redshifts and velocity-dispersion measurements (an 851% increase in velocity dispersions over the original literature compilation), enabling archival-only cosmological analyses of hundreds of lenses.

Reading between the lines

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

  • If the database is actively maintained as new lens discoveries appear, it could become a standard calibration set for automated lens finders; that wider adoption is not something the paper can guarantee.
  • The 0.044 gap between the aperture-corrected and uncorrected $\gamma_{\mathrm{PPN}}$ estimates is larger than the quoted statistical errors, hinting that the true systematic floor of this measurement may be set by assumptions about galaxy profiles and aperture corrections rather than by sample size.
  • A natural extension, not pursued here, would be to measure velocity dispersions for the hundreds of LaStBeRu lenses that lack them, which could roughly halve the uncertainty on $\gamma_{\mathrm{PPN}}$ and test whether the gravitational slip parameter is constant across redshift or environment.
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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

3 major / 6 minor

Summary. This paper presents LaStBeRu, a consolidated database of 31,569 unique strong-lensing candidates compiled from the literature, cross-matched with photometric and spectroscopic catalogs from seven wide-field surveys, and accompanied by 366,905 image cutouts in two angular sizes. The catalog preserves provenance for every value, merges duplicates by JNAME with explicit 1 and 10 arcsec rules, and applies objective quality filters (seeing, exposure time, empty-cutout removal) for image selection. As a demonstration application, the authors construct a 206-system ground-based sample (LaStBeRu_cosmo_ground) of isolated early-type galaxy lenses with SDSS velocity dispersions and use the Schwab et al. (2010) method to constrain the PPN parameter γ_PPN, reporting 1.023 ± 0.028 for the ground sample and 1.034 ± 0.025 for the combined sample of 280 unique systems, which they describe as the tightest constraint from this type of analysis.

Significance. The catalog construction is a substantial and careful contribution: provenance is tracked for each measurement, the JNAME merging rules are explicit, cross-matching uses a 1 arcsec nearest-neighbour criterion, cutout selection uses header-based seeing and exposure criteria, empty cutouts are removed with SEP, and the data and code are publicly available. This resource should be useful for LSST preparation, machine-learning training, and follow-up target selection. The γ_PPN application is a reasonable demonstration of the database, but the headline precision claim is not yet supported by the error budget because the posterior for γ_PPN is conditioned on prior-dominated population parameters (β, δ) and the quoted uncertainty omits the corresponding systematic. The catalog itself is not affected by this issue, but the application's conclusions need revision before publication.

major comments (3)
  1. [7.1.2, Eqs. (8)-(9)] The paper states that 'Posteriors for β and δ were prior-dominated.' Because γ_PPN is inferred jointly with the global power-law slopes α, δ and anisotropy β, a prior-dominated β and δ means the γ_PPN posterior is effectively conditional on the prior means ⟨β⟩=0.18 and ⟨δ⟩=2.40. The quoted errors (1.023±0.028 and 1.034±0.025) propagate the prior widths but not the uncertainty in the prior locations, nor any intrinsic population scatter. The comparison between aperture-corrected (1.023±0.028) and uncorrected (0.979±0.029) results shows a shift of ~0.04, comparable to the statistical error, which indicates a systematic not captured by the 3% line-of-sight term in Eq. (9). The authors should either marginalize over population hyper-parameters, add a systematic term, or explicitly frame the result as conditional on the adopted priors and avoid calling it the tightest constraint.
  2. [Abstract] The abstract claims 'the most stringent constraint on γ_PPN', but Section 7.1.2 correctly qualifies this as 'the tightest constraint on γ_PPN from this type of analysis to date'. Solar-system tests constrain γ_PPN to ~10^-5, orders of magnitude tighter than the values reported here. The abstract should carry the same qualifier as the body text, or the claim will be misleading.
  3. [8 and 7.1.2] The concluding remarks state 'For the first time, we used priors on the brightness and density slopes from the same data, providing a more self-consistent analysis compared to previous works.' However, Section 7.1.2 lists Gaussian priors ⟨α⟩=2.00±0.08, ⟨β⟩=0.18±0.13, ⟨δ⟩=2.40±0.11 without any derivation from the LaStBeRu data. If these priors are taken from the literature, the claim in Section 8 is unsupported and should be removed or corrected; if they were estimated from the sample, the estimation procedure must be described. This inconsistency is central to the claimed novelty of the γ_PPN analysis.
minor comments (6)
  1. [7.1.1] The text writes 'SWEELS' catalog, but the standard acronym for the Sloan WFC Edge-on Late-type Lens Survey is SWELLS (Treu et al. 2011).
  2. [7.1.1] The sentence 'which is relevant for the modeling and has not typically been used in previous statistical analyses of this type' is repeated twice in the same paragraph.
  3. [7.1.2] The MCMC setup (64 walkers, 15,000 steps, 500 burn-in) is reported, but no convergence diagnostics (e.g., autocorrelation time or Gelman-Rubin statistic) are given; please report them for the quoted γ_PPN values.
  4. [Figure 13] The numbers in the Venn-diagram labels are not explained in the caption; please define what each number represents (e.g., number of systems in each intersection).
  5. [5.1] The empty-cutout fractions (11% for CFHTLenS, 18% for RCSLenS) should be reported as fractions of attempted cutouts, and the authors should comment on whether the removal of empty cutouts biases the final catalog against faint lenses or poor-seeing images.
  6. [6] The data are hosted on a GitHub repository; for long-term reproducibility, a versioned DOI or Zenodo record would be preferable.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the gamma_PPN constraint is a genuine model-based inference; the database is an explicit compilation, and the prior-dominated nuisance parameters are a caveat rather than a reduction.

full rationale

The paper's primary product is the LaStBeRu database and cutout collection, which is explicitly assembled from literature catalogs and survey cross-matches; no derivation is claimed from first principles, so there is no self-definitional or renamed-known-result circularity in that core deliverable. The gamma_PPN application in Section 7.1.2 uses Equation (8) taken from Schwab et al. (2010) and Cao et al. (2017), equating the theoretical aperture-corrected velocity dispersion to the observed one. gamma_PPN enters as an independent model parameter through the (1+gamma_PPN) factor; it is not defined by, nor a relabeling of, any fitted input. The Gaussian priors on alpha, beta, and delta are external literature values, and the paper explicitly states that the posteriors for beta and delta were prior-dominated. That is a genuine prior-sensitivity limitation, but it is not circular: the inference would still be a data-informed fit if the priors were replaced by other assumptions, and no equation reduces to its own input by construction. The claim of an 'independent' sample is explicitly defined in terms of non-overlap with previous compilations (Figure 13: 103 systems not previously used), which is a disclosed sample-selection statement rather than a circularity. The only self-references are to 'Franca et al., in preparation' for future SOAR/Gemini follow-up work; these are not load-bearing for the present archival-data constraint. Accordingly, no step in the derivation chain reduces to its inputs by definition, and the circularity score is 0.

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

The γ_PPN inference rests on a small set of adopted prior values (α, δ, β), an external aperture-correction calibration ζ, and several domain assumptions including power-law homology, η = γ_PPN, Planck cosmology, and a genuine-lens sample. The database itself introduces no new physical entities. The main non-data content is the prior and law structure of the Jeans plus lensing model.

free parameters (5)
  • α (total mass density slope prior mean) = 2.00 ± 0.08
    Global prior used in the Jeans and lensing model (Equations 2 and 8). If the true slope distribution differs from 2.00, the γ_PPN estimate shifts; the paper does not fit α to the lensing plus dynamics data.
  • δ (luminosity density slope prior mean) = 2.40 ± 0.11
    Global prior used in Equation 3 and Equation 8; the posterior for δ is prior-dominated.
  • β (orbital anisotropy prior mean) = 0.18 ± 0.13
    Global prior used in Equation 4 and Equation 8; the posterior for β is prior-dominated, so the γ_PPN constraint partly inherits this prior.
  • ζ (aperture correction exponent) = -0.066 ± 0.035
    External calibration from Cappellari et al. 2006 used in Equation 7 to correct SDSS velocity dispersions; uncertainty is propagated but the value is adopted, not fitted.
  • 3% line-of-sight systematic error = 0.03
    Added in quadrature in Equation 9; chosen by hand to represent LOS structure systematics, not measured in this sample.
assumptions (6)
  • domain assumption The lens galaxies form a homologous population described by spherical power-law total density and luminosity profiles with globally shared slopes α, δ, and constant anisotropy β.
    Equations 2 to 6; the MCMC assumes global X across all 206 systems. If homology fails, γ_PPN is biased.
  • domain assumption The observed Einstein radius θ_E, taken from parametric lens models in the literature, corresponds to the same mass scale probed by stellar velocity dispersion.
    Equation 8 and Section 4's θ_E consolidation; model-derived θ_E values are assumed to be unbiased and comparable across surveys.
  • domain assumption The gravitational slip η is equivalent to γ_PPN under the assumed scales.
    Section 7.1.2, citing Toniato and Rodrigues 2021; this identification is needed to interpret the fit as a PPN parameter.
  • domain assumption Flat ΛCDM with Planck 2018 cosmological parameters describes the distance ratio D_S/D_LS.
    Section 7.1.2 MCMC; the γ_PPN posterior depends on this assumed cosmology.
  • domain assumption The 206 systems in LaStBeRu_cosmo_ground are genuine isolated ETG lenses with sufficient modeling quality.
    Section 7.1.1 uses visual inspection (lensrater) and excludes groups, clusters, and late-type lenses; if contaminants passed, the γ_PPN inference would be biased.
  • domain assumption The θ_E values from the consolidated catalog are compatible across surveys and were derived from parametric lens models with comparable assumptions.
    Section 4 filters to model-derived θ_E and applies a publication-date hierarchy; systematics from heterogeneous modeling are not fully propagated.

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Pith. "Pith review of The Last Stand Before Rubin: a consolidated sample of strong lensing systems in wide-field surveys." pith.science (2026). https://pith.science/paper/NLBQYOK2

@misc{pith2026250909798,
  author       = {Pith},
  title        = {Pith review of: The Last Stand Before Rubin: a consolidated sample of strong lensing systems in wide-field surveys},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NLBQYOK2}},
  note         = {Machine review of arXiv:2509.09798}
}
abstract

As the Vera Rubin Observatory begins its ten-year survey in 2025, it will probe key observables such as strong lensing (SL) by galaxies and clusters. In preparation for this new era, we assemble an extensive compilation of SL candidate systems from the literature, comprising over 30,000 unique objects that can be used as a watch list of known systems. By cross-matching this sample with photometric and spectroscopic catalogs, we construct two value-added tables containing key parameters for SL analysis, including lens and source redshifts and lens velocity dispersions $\sigma_v$. As a preparation for Rubin, we generate image cutouts for these systems in existing wide-field surveys with subarcsecond seeing, namely CFHTLens, CS82, RCSLens, KiDS, HSC, DES, and DESI Legacy. This sample, dubbed the "Last Stand Before Rubin" (LaStBeRu), has a myriad of applications, from using archival data to selections for follow-up projects and training of machine learning algorithms. As an application, we perform a test of General Relativity using these data, combining the effects of motion of massless particles (through SL modeling) and non-relativistic bodies through $\sigma_v$, which allow one to set constraints on the Post-Newtonian parameter $\gamma_\mathrm{PPN}$. Using the LaStBeRu database, we present an independent test of $\gamma_\mathrm{PPN}$ (distinct from previous analyses) and, for the first time, we present such a test exclusively with systems identifiable in ground-based images. By combining these data with the previously published samples, we obtain the most stringent constraint on $\gamma_\mathrm{PPN}$. Our results are consistent with GR at the $\sim$~1-$\sigma$ level and with the previous results from the literature.

Figures

Figures reproduced from arXiv: 2509.09798 by the authors.

Figure 1
Figure 1. This image summarizes the steps to build the “slcomp” framework, which aggregates many lensed systems into a single database. automated arcfinding techniques in imaging surveys have also led to the discovery of some SL systems (Estrada et al. 2007; Hennawi et al. 2008; Diehl et al. 2009; Diehl et al. 2017; O’Donnell et al. 2021). However, recent advances in Machine Learning (ML) techniques in astronomy have led to r… view at source ↗
Figure 2
Figure 2. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Footprints of the wide-field surveys that are used as sources for matching the photometric information of the SL systems. Each point repre￾sents one system from the SL compilation described in Section 2.2. With this match, we were able to link objects from the HSC internal catalog, enabling the use of other value-added catalogs for redshifts from tables“photoz_demp”, “photoz_dnnz”, and “photoz_mizuki”. We then filte… view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Footprints of all spectroscopic surveys used to match objects from the literature are categorized into small (top), medium (mid), and large (bot￾tom) areas. The dots represent our matched systems, overlaid on the respective survey footprints. (“SpecObj” table from “Spe…
Figure 5
Figure 5. Figure 5: Workflow for assigning a single, adopted velocity dispersion (𝜎𝑣) and lens redshift (𝑧𝐿). The process follows a clear hierarchy, prioritizing spectroscopic data from our cross-matches and literature compilation over photometric redshifts. able in the original SL catalo…
Figure 9
Figure 9. Figure 9: Colored images of J010238.3+015856.8 from HSC images using make_lupton_rgb, STIFF, and Trilogy (from left to right). 5.2 Image Combination Combining astronomical monochromatic (single-band) images into color images (usually composed of red, green, and blue) is a tricky…
Figure 8
Figure 8. Figure 8: Intersections of unique systems with cutouts of 20′′ are available in the seven imaging surveys included in this work. The numbers on the bottom left panel show the number of unique objects with cutouts in each survey. The lines indicate the intersection of the surveys…
Figure 10
Figure 10. Figure 10: Top: Examples of different strong lensing systems in each survey. Bottom: system J015750.4−003818.5 seen in various surveys. DES LSB: DES Legacy LSB: Legacy [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: Example of artifacts of the object J213822.0+011648.9 observed in two surveys with Trilogy combined images and LSB images. rameters used to combine the images, some artifacts, such as the fake rings shown in [PITH_FULL_IMAGE:figures/full_fig_p011_11.png]
Figure 12
Figure 12. Figure 12 [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]
Figure 13
Figure 13. Figure 13: Intersections between the LaStBeRu_cosmo_ground sample and the samples compiled in Cao et al. (2015) and Chen et al. (2019a). For the LaStBeRu_cosmo_ground sample, we used the emcee (Foreman-Mackey et al. 2013) package for Markov Chain Monte Carlo (MCMC) analysis, ass…
Figure 14
Figure 14. Figure 14: Estimated values for 𝛾PPN reported from the literature and from this work. The black dots represent results without aperture correction, while the red dots indicate results obtained, including this correction. and cosmological applications. One may use this purely arc…
Figure 15
Figure 15. Figure 15: Example of a shared spreadsheet with data from LaStBeRu and each available mosaic of cutouts for the observational candidates. a clear SL morphology compatible with images from a single ellip￾tical lens to enable accurate inverse reconstruction of the lens mass distri…
Figure 16
Figure 16. Figure 16: 10 SL candidate systems from the selected subsample. All exhibit characteristics that may resemble SL features in the HSC data, such as multiple images and multiply peaked gravitational arcs or rings, which allow for the inverse modeling of these systems to determine …

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Works this paper leans on

203 extracted references · 31 canonical work pages

  1. [1]

    Abbott T. M. C., et al., 2021, @doi [ ] 10.3847/1538-4365/ac00b3 , https://ui.adsabs.harvard.edu/abs/2021ApJS..255...20A 255, 20

    doi:10.3847/1538-4365/ac00b3 2021

  2. [2]

    Abdurro'uf et al., 2022, @doi [ ] 10.3847/1538-4365/ac4414 , https://ui.adsabs.harvard.edu/abs/2022ApJS..259...35A 259, 35

    doi:10.3847/1538-4365/ac4414 2022

  3. [3]

    Aihara H., et al., 2018, @doi [ ] 10.1093/pasj/psx066 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...4A 70, S4

    doi:10.1093/pasj/psx066 2018

  4. [4]

    Aihara H., et al., 2022, @doi [ ] 10.1093/pasj/psab122 , https://ui.adsabs.harvard.edu/abs/2022PASJ...74..247A 74, 247

    doi:10.1093/pasj/psab122 2022

  5. [5]

    S., Tucker D

    Allam S. S., Tucker D. L., Lin H., Diehl H. T., Annis J., Buckley-Geer E. J., Frieman J. A., 2007, @doi [ ] 10.1086/519520 , https://ui.adsabs.harvard.edu/abs/2007ApJ...662L..51A 662, L51

    doi:10.1086/519520 2007

  6. [6]

    Amvrosiadis A., et al., 2018, @doi [ ] 10.1093/mnras/sty138 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.4939A 475, 4939

    work page doi:10.1093/mnras/sty138 2018

  7. [7]

    arXiv:2404.01918

    Amvrosiadis A., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2404.01918 , https://ui.adsabs.harvard.edu/abs/2024arXiv240401918A p. arXiv:2404.01918

    arXiv doi:10.48550/arxiv.2404.01918 2024

  8. [8]

    Anau Montel N., Coogan A., Correa C., Karchev K., Weniger C., 2022, @doi [MNRAS] 10.1093/mnras/stac3215 , 518, 2746

    doi:10.1093/mnras/stac3215 2022

Show all 203 references
  1. [9]

    Astropy Collaboration et al., 2022, @doi [ ] 10.3847/1538-4357/ac7c74 , https://ui.adsabs.harvard.edu/abs/2022ApJ...935..167A 935, 167

    2022 doi

  2. [10]

    W., Treu T., Bolton A

    Auger M. W., Treu T., Bolton A. S., Gavazzi R., Koopmans L. V. E., Marshall P. J., Bundy K., Moustakas L. A., 2009, @doi [ ] 10.1088/0004-637X/705/2/1099 , https://ui.adsabs.harvard.edu/abs/2009ApJ...705.1099A 705, 1099

    2009 doi

  3. [11]

    K., et al., 2010, @doi [ ] 10.1111/j.1365-2966.2010.16282.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.404...86B 404, 86

    Baldry I. K., et al., 2010, @doi [ ] 10.1111/j.1365-2966.2010.16282.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.404...86B 404, 86

    2010

  4. [12]

    K., et al., 2018, @doi [ ] 10.1093/mnras/stx3042 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474.3875B 474, 3875

    Baldry I. K., et al., 2018, @doi [ ] 10.1093/mnras/stx3042 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474.3875B 474, 3875

    2018 doi

  5. [13]

    Barbary K., 2016, @doi [Journal of Open Source Software] 10.21105/joss.00058 , https://ui.adsabs.harvard.edu/abs/2016JOSS....1...58B 1, 58

    2016 doi

  6. [14]

    S., Smith K., 2011, @doi [Computing in Science and Engineering] 10.1109/MCSE.2010.118 , https://ui.adsabs.harvard.edu/abs/2011CSE....13b..31B 13, 31

    Behnel S., Bradshaw R., Citro C., Dalcin L., Seljebotn D. S., Smith K., 2011, @doi [Computing in Science and Engineering] 10.1109/MCSE.2010.118 , https://ui.adsabs.harvard.edu/abs/2011CSE....13b..31B 13, 31

    2011 doi

  7. [15]

    Bernardi M., et al., 2003, @doi [ ] 10.1086/367776 , https://ui.adsabs.harvard.edu/abs/2003AJ....125.1817B 125, 1817

    2003 doi

  8. [16]

    Bertin E., 2012, in Ballester P., Egret D., Lorente N. P. F., eds, Astronomical Society of the Pacific Conference Series Vol. 461, Astronomical Data Analysis Software and Systems XXI. p. 263

    2012

  9. [17]

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

    1996 doi

  10. [18]

    Bertschinger E., 2011, @doi [Philosophical Transactions of the Royal Society of London Series A] 10.1098/rsta.2011.0369 , https://ui.adsabs.harvard.edu/abs/2011RSPTA.369.4947B 369, 4947

    2011

  11. [19]

    S., 2005, @doi [ ] 10.1146/annurev.astro.41.082801.100251 , https://ui.adsabs.harvard.edu/abs/2005ARA&A..43..293B 43, 293

    Bessell M. S., 2005, @doi [ ] 10.1146/annurev.astro.41.082801.100251 , https://ui.adsabs.harvard.edu/abs/2005ARA&A..43..293B 43, 293

    2005

  12. [20]

    L., Cassisi S., Walker A

    Bettinelli M., Simioni M., Aparicio A., Hidalgo S. L., Cassisi S., Walker A. R., Piotto G., Valdes F., 2016, @doi [ ] 10.1093/mnrasl/slw097 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.461L..67B 461, L67

    2016 doi

  13. [21]

    Princeton University Press, @doi 10.2307/j.ctv1nxcw51

    Binney J., Merrifield M., 1998, Galactic Astronomy. Princeton University Press, @doi 10.2307/j.ctv1nxcw51

    1998 doi

  14. [22]

    Birrer S., Amara A., 2018, @doi [PDU] 10.1016/j.dark.2018.11.002 , https://ui.adsabs.harvard.edu/abs/2018PDU....22..189B 22, 189

    2018 doi

  15. [23]

    521, Astronomical Data Analysis Software and Systems XXVI

    Boch T., 2019, in Molinaro M., Shortridge K., Pasian F., eds, Astronomical Society of the Pacific Conference Series Vol. 521, Astronomical Data Analysis Software and Systems XXVI. p. 487

    2019

  16. [24]

    Boch T., Pineau F., Derriere S., 2012, in Ballester P., Egret D., Lorente N. P. F., eds, Astronomical Society of the Pacific Conference Series Vol. 461, Astronomical Data Analysis Software and Systems XXI. p. 291

    2012

  17. [25]

    B \"o hringer H., et al., 2000, @doi [ ] 10.1086/313427 , https://ui.adsabs.harvard.edu/abs/2000ApJS..129..435B 129, 435

    2000 doi

  18. [26]

    B \"o hringer H., et al., 2004, @doi [ ] 10.1051/0004-6361:20034484 , https://ui.adsabs.harvard.edu/abs/2004A&A...425..367B 425, 367

    2004 doi

  19. [27]

    S., Rappaport S., Burles S., 2006a, @doi [ ] 10.1103/PhysRevD.74.061501 , https://ui.adsabs.harvard.edu/abs/2006PhRvD..74f1501B 74, 061501

    Bolton A. S., Rappaport S., Burles S., 2006a, @doi [ ] 10.1103/PhysRevD.74.061501 , https://ui.adsabs.harvard.edu/abs/2006PhRvD..74f1501B 74, 061501

    doi

  20. [28]

    S., Burles S., Koopmans L

    Bolton A. S., Burles S., Koopmans L. V. E., Treu T., Moustakas L. A., 2006b, @doi [ ] 10.1086/498884 , http://adsabs.harvard.edu/abs/2006ApJ...638..703B 638, 703

    doi

  21. [29]

    S., Burles S., Koopmans L

    Bolton A. S., Burles S., Koopmans L. V. E., Treu T., Gavazzi R., Moustakas L. A., Wayth R., Schlegel D. J., 2008, @doi [ ] 10.1086/589327 , https://ui.adsabs.harvard.edu/abs/2008ApJ...682..964B 682, 964

    2008 doi

  22. [30]

    S., et al., 2012, @doi [ ] 10.1088/0004-637X/757/1/82 , https://ui.adsabs.harvard.edu/abs/2012ApJ...757...82B 757, 82

    Bolton A. S., et al., 2012, @doi [ ] 10.1088/0004-637X/757/1/82 , https://ui.adsabs.harvard.edu/abs/2012ApJ...757...82B 757, 82

    2012 doi

  23. [31]

    R., et al., 2022, @doi [arXiv e-prints] 10.1093/mnras/stac2047 , https://ui.adsabs.harvard.edu/abs/2022arXiv220309536B p

    Bom C. R., et al., 2022, @doi [arXiv e-prints] 10.1093/mnras/stac2047 , https://ui.adsabs.harvard.edu/abs/2022arXiv220309536B p. arXiv:2203.09536

    2022 arXiv

  24. [32]

    Borsato E., et al., 2024, @doi [ ] 10.1093/mnras/stad3381 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.6222B 528, 6222

    2024 doi

  25. [33]

    H., 2017, xmatch, https://github.com/chbrandt/xmatch

    Brandt C. H., 2017, xmatch, https://github.com/chbrandt/xmatch

    2017

  26. [34]

    R., et al., 2012, @doi [ ] 10.1088/0004-637X/744/1/41 , http://adsabs.harvard.edu/abs/2012ApJ...744...41B 744, 41

    Brownstein J. R., et al., 2012, @doi [ ] 10.1088/0004-637X/744/1/41 , http://adsabs.harvard.edu/abs/2012ApJ...744...41B 744, 41

    2012 doi

  27. [35]

    Ca \ n ameras R., et al., 2020, @doi [ ] 10.1051/0004-6361/202038219 , https://ui.adsabs.harvard.edu/abs/2020A&A...644A.163C 644, A163

    2020 doi

  28. [36]

    Ca \ n ameras R., et al., 2021, @doi [ ] 10.1051/0004-6361/202141758 , https://ui.adsabs.harvard.edu/abs/2021A&A...653L...6C 653, L6

    2021 doi

  29. [37]

    A., et al., 2007, @doi [ ] 10.1051/0004-6361:20065810 , http://adsabs.harvard.edu/abs/2007A

    Cabanac R. A., et al., 2007, @doi [ ] 10.1051/0004-6361:20065810 , http://adsabs.harvard.edu/abs/2007A

    2007 doi

  30. [38]

    Cao S., Biesiada M., Gavazzi R., Pi \'o rkowska A., Zhu Z.-H., 2015, @doi [ ] 10.1088/0004-637X/806/2/185 , https://ui.adsabs.harvard.edu/abs/2015ApJ...806..185C 806, 185

    2015 doi

  31. [39]

    Cao S., Biesiada M., Yao M., Zhu Z.-H., 2016, @doi [ ] 10.1093/mnras/stw932 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.461.2192C 461, 2192

    2016 doi

  32. [40]

    Cao S., Li X., Biesiada M., Xu T., Cai Y., Zhu Z.-H., 2017, @doi [ ] 10.3847/1538-4357/835/1/92 , https://ui.adsabs.harvard.edu/abs/2017ApJ...835...92C 835, 92

    2017 doi

  33. [41]

    Cao X., Li R., Shu Y., Mao S., Kneib J.-P., Gao L., 2020, @doi [ ] 10.1093/mnras/staa3058 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.3610C 499, 3610

    2020 doi

  34. [42]

    Cappellari M., et al., 2006, @doi [MNRAS] 10.1111/j.1365-2966.2005.09981.x , 366, 1126

    2006

  35. [43]

    Chan J. H. H., et al., 2020, @doi [ ] 10.1051/0004-6361/201937030 , https://ui.adsabs.harvard.edu/abs/2020A&A...636A..87C 636, A87

    2020 doi

  36. [44]

    Chen Y., Li R., Shu Y., Cao X., 2019a, @doi [ ] 10.1093/mnras/stz1902 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.3745C 488, 3745

    doi

  37. [45]

    Chen G. C. F., et al., 2019b, @doi [ ] 10.1093/mnras/stz2547 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.1743C 490, 1743

    doi

  38. [46]

    Coe D., 2015, Trilogy: FITS image conversion software, Astrophysics Source Code Library, record ascl:1508.009 ( @eprint ascl 1508.009 )

    2015

  39. [47]

    Collaboration T. D. E. S., 2005, @doi [arXiv:astro-ph/0510346] 10.48550/arXiv.astro-ph/0510346 , http://adsabs.harvard.edu/abs/2005astro.ph.10346T pp astro--ph/0510346

    2005 doi arXiv

  40. [48]

    Colless M., et al., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04902.x , https://ui.adsabs.harvard.edu/abs/2001MNRAS.328.1039C 328, 1039

    2001

  41. [49]

    E., 2015, @doi [ApJ] 10.1088/0004-637X/811/1/20 , 811, 20

    Collett T. E., 2015, @doi [ApJ] 10.1088/0004-637X/811/1/20 , 811, 20

    2015 doi

  42. [50]

    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

    2018 doi

  43. [52]

    J., et al., 2012, in Peck A

    Cuillandre J.-C. J., et al., 2012, in Peck A. B., Seaman R. L., Comeron F., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 8448, Observatory Operations: Strategies, Processes, and Systems IV. p. 84480M, @doi 10.1117/12.925584

    2012 doi

  44. [53]

    S., Sodr \'e Laerte J., Kneib J.-P., Campusano L

    Cypriano E. S., Sodr \'e Laerte J., Kneib J.-P., Campusano L. E., 2004, @doi [ ] 10.1086/422896 , https://ui.adsabs.harvard.edu/abs/2004ApJ...613...95C 613, 95

    2004 doi

  45. [54]

    Dey A., et al., 2019, @doi [ ] 10.3847/1538-3881/ab089d , https://ui.adsabs.harvard.edu/abs/2019AJ....157..168D 157, 168

    2019 doi

  46. [55]

    T., et al., 2009, @doi [ ] 10.1088/0004-637X/707/1/686 , https://ui.adsabs.harvard.edu/abs/2009ApJ...707..686D 707, 686

    Diehl H. T., et al., 2009, @doi [ ] 10.1088/0004-637X/707/1/686 , https://ui.adsabs.harvard.edu/abs/2009ApJ...707..686D 707, 686

    2009 doi

  47. [56]

    T., et al., 2017, @doi [ ] 10.3847/1538-4365/aa8667 , https://ui.adsabs.harvard.edu/abs/2017ApJS..232...15D 232, 15

    Diehl H. T., et al., 2017, @doi [ ] 10.3847/1538-4365/aa8667 , https://ui.adsabs.harvard.edu/abs/2017ApJS..232...15D 232, 15

    2017 doi

  48. [58]

    Drlica-Wagner A., et al., 2022, @doi [ ] 10.3847/1538-4365/ac78eb , https://ui.adsabs.harvard.edu/abs/2022ApJS..261...38D 261, 38

    2022 doi

  49. [61]

    Estrada J., et al., 2007, @doi [ ] 10.1086/512599 , http://adsabs.harvard.edu/abs/2007ApJ...660.1176E 660, 1176

    2007 doi

  50. [62]

    Etherington A., et al., 2022, @doi [ ] 10.1093/mnras/stac2639 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.3275E 517, 3275

    2022 doi

  51. [63]

    Etherington A., et al., 2023, @doi [ ] 10.1093/mnras/stad582 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.6005E 521, 6005

    2023 doi

  52. [64]

    Etherington A., et al., 2024, @doi [ ] 10.1093/mnras/stae1375 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.531.3684E 531, 3684

    2024 doi

  53. [65]

    arXiv:2503.15324

    Euclid Collaboration et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2503.15324 , https://ui.adsabs.harvard.edu/abs/2025arXiv250315324E p. arXiv:2503.15324

    2025 doi arXiv

  54. [66]

    Faure C., et al., 2008, @doi [ ] 10.1086/526426 , http://adsabs.harvard.edu/abs/2008ApJS..176...19F 176, 19

    2008 doi

  55. [67]

    P., Samsing J., 2009, @doi [ ] 10.1088/0004-637X/696/2/1319 , https://ui.adsabs.harvard.edu/abs/2009ApJ...696.1319F 696, 1319

    F \'e ron C., Hjorth J., McKean J. P., Samsing J., 2009, @doi [ ] 10.1088/0004-637X/696/2/1319 , https://ui.adsabs.harvard.edu/abs/2009ApJ...696.1319F 696, 1319

    2009 doi

  56. [68]

    Focardi P., Rossetti E., 2015, @doi [ ] 10.1051/0004-6361/201526364 , https://ui.adsabs.harvard.edu/abs/2015A&A...581A..99F 581, A99

    2015 doi

  57. [69]

    W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    Foreman-Mackey D., Hogg D. W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    2013 doi

  58. [70]

    Furlanetto C., et al., 2013, @doi [ ] 10.1093/mnras/stt380 , http://adsabs.harvard.edu/abs/2013MNRAS.tmp.1144F 432, 73

    2013 doi

  59. [71]

    E., M \'e ndez-Abreu J., S \'a nchez S

    Galbany L., Collett T. E., M \'e ndez-Abreu J., S \'a nchez S. F., Anderson J. P., Kuncarayakti H., 2018, @doi [ ] 10.1093/mnras/sty1448 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479..262G 479, 262

    2018 doi

  60. [72]

    D., Koopmans L

    Gavazzi R., Treu T., Rhodes J. D., Koopmans L. V. E., Bolton A. S., Burles S., Massey R. J., Moustakas L. A., 2007, @doi [ ] 10.1086/519237 , https://ui.adsabs.harvard.edu/abs/2007ApJ...667..176G 667, 176

    2007 doi

  61. [73]

    Gavazzi R., Treu T., Koopmans L. V. E., Bolton A. S., Moustakas L. A., Burles S., Marshall P. J., 2008, @doi [ApJ] 10.1086/529541 , 677, 1046

    2008 doi

  62. [74]

    E., et al., 2015, @doi [ ] 10.1093/mnras/stv1243 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452..502G 452, 502

    Geach J. E., et al., 2015, @doi [ ] 10.1093/mnras/stv1243 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452..502G 452, 502

    2015 doi

  63. [75]

    G., Gladders M

    Gilbank D. G., Gladders M. D., Yee H. K. C., Hsieh B. C., 2011, @doi [ ] 10.1088/0004-6256/141/3/94 , http://adsabs.harvard.edu/abs/2011AJ....141...94G 141, 94

    2011 doi

  64. [76]

    Gilman Birrer, S. Treu, T. 2020, @doi [A&A] 10.1051/0004-6361/202038829 , 642, A194

    2020 doi

  65. [77]

    M., Maccacaro T., Schild R

    Gioia I. M., Maccacaro T., Schild R. E., Wolter A., Stocke J. T., Morris S. L., Henry J. P., 1990, @doi [ ] 10.1086/191426 , https://ui.adsabs.harvard.edu/abs/1990ApJS...72..567G 72, 567

    1990 doi

  66. [78]

    D., Yee H

    Gladders M. D., Yee H. K. C., 2005, @doi [ ] 10.1086/427327 , https://ui.adsabs.harvard.edu/abs/2005ApJS..157....1G 157, 1

    2005 doi

  67. [79]

    H., Zaritsky D., Dalcanton J

    Gonzalez A. H., Zaritsky D., Dalcanton J. J., Nelson A., 2001, @doi [ ] 10.1086/322541 , https://ui.adsabs.harvard.edu/abs/2001ApJS..137..117G 137, 117

    2001 doi

  68. [80]

    Goobar A., et al., 2017, @doi [Science] 10.1126/science.aal2729 , https://ui.adsabs.harvard.edu/abs/2017Sci...356..291G 356, 291

    2017 doi

  69. [81]

    Graham M., Plante R., Tody D., Fitzpatrick M., 2014, PyVO: Python access to the Virtual Observatory, Astrophysics Source Code Library, record ascl:1402.004 ( @eprint ascl 1402.004 )

    2014

  70. [82]

    Grigorev K., 2018, pyvenn, https://pypi.org/project/venn/

    2018

  71. [83]

    R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , 585, 357–362

    Harris C. R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , 585, 357–362

    2020 doi

  72. [84]

    F., et al., 2008, @doi [ ] 10.1088/0004-6256/135/2/664 , http://adsabs.harvard.edu/abs/2008AJ....135..664H 135, 664

    Hennawi J. F., et al., 2008, @doi [ ] 10.1088/0004-6256/135/2/664 , http://adsabs.harvard.edu/abs/2008AJ....135..664H 135, 664

    2008 doi

  73. [85]

    Heymans C., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21952.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427..146H 427, 146

    2012

  74. [86]

    Hildebrandt H., et al., 2016, @doi [ ] 10.1093/mnras/stw2013 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.463..635H 463, 635

    2016 doi

  75. [87]

    J., Verma A., More A., Ca \ n ameras R., Jaelani A

    Holloway P., Marshall P. J., Verma A., More A., Ca \ n ameras R., Jaelani A. T., Ishida Y., Wong K. C., 2023, @doi [arXiv e-prints] 10.48550/arXiv.2311.07455 , https://ui.adsabs.harvard.edu/abs/2023arXiv231107455H p. arXiv:2311.07455

    2023 doi arXiv

  76. [88]

    W., et al., 2015, @doi [ ] 10.1093/mnras/stv589 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.449.4277H 449, 4277

    Holwerda B. W., et al., 2015, @doi [ ] 10.1093/mnras/stv589 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.449.4277H 449, 4277

    2015 doi

  77. [89]

    Huang X., et al., 2020, @doi [ ] 10.3847/1538-4357/ab7ffb , https://ui.adsabs.harvard.edu/abs/2020ApJ...894...78H 894, 78

    2020 doi

  78. [90]

    Huang X., et al., 2021, @doi [ ] 10.3847/1538-4357/abd62b , https://ui.adsabs.harvard.edu/abs/2021ApJ...909...27H 909, 27

    2021 doi

  79. [91]

    P., Geller M

    Huchra J. P., Geller M. J., de Lapparent V., Corwin Harold G. J., 1990, @doi [ ] 10.1086/191423 , https://ui.adsabs.harvard.edu/abs/1990ApJS...72..433H 72, 433

    1990 doi

  80. [92]

    P., et al., 2012, @doi [ ] 10.1088/0067-0049/199/2/26 , https://ui.adsabs.harvard.edu/abs/2012ApJS..199...26H 199, 26

    Huchra J. P., et al., 2012, @doi [ ] 10.1088/0067-0049/199/2/26 , https://ui.adsabs.harvard.edu/abs/2012ApJS..199...26H 199, 26

    2012 doi

  81. [93]

    D., 2007, @doi [Computing in Science & Engineering] 10.1109/MCSE.2007.55 , 9, 90

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

    2007 doi

  82. [94]

    J.] 10.3847/1538-4357/ab042c , 873, 111

    Ivezi\'c v., et al., 2019, @doi [Astrophys. J.] 10.3847/1538-4357/ab042c , 873, 111

    2019 doi

  83. [95]

    Jacobs C., et al., 2019a, @doi [ ] 10.3847/1538-4365/ab26b6 , https://ui.adsabs.harvard.edu/abs/2019ApJS..243...17J 243, 17

    doi

  84. [96]

    Jacobs C., et al., 2019b, @doi [ ] 10.1093/mnras/stz272 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.5330J 484, 5330

    doi

  85. [97]

    T., et al., 2020, @doi [ ] 10.1093/mnras/staa1062 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.1291J 495, 1291

    Jaelani A. T., et al., 2020, @doi [ ] 10.1093/mnras/staa1062 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.1291J 495, 1291

    2020 doi

  86. [98]

    T., et al., 2021, @doi [ ] 10.1093/mnras/stab145 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.1487J 502, 1487

    Jaelani A. T., et al., 2021, @doi [ ] 10.1093/mnras/stab145 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.1487J 502, 1487

    2021 doi

  87. [100]

    Jorgensen I., Franx M., Kjaergaard P., 1995, @doi [ ] 10.1093/mnras/276.4.1341 , https://ui.adsabs.harvard.edu/abs/1995MNRAS.276.1341J 276, 1341

    1995 doi

  88. [101]

    Kausch W., Schindler S., Erben T., Wambsganss J., Schwope A., 2010, @doi [ ] 10.1051/0004-6361/200811066 , http://adsabs.harvard.edu/abs/2010A

    2010 doi

  89. [102]

    S., et al., 2012, @doi [ ] 10.1088/0067-0049/200/1/8 , https://ui.adsabs.harvard.edu/abs/2012ApJS..200....8K 200, 8

    Kochanek C. S., et al., 2012, @doi [ ] 10.1088/0067-0049/200/1/8 , https://ui.adsabs.harvard.edu/abs/2012ApJS..200....8K 200, 8

    2012 doi

  90. [103]

    Koopmans L. V. E., 2006, in Mamon G. A., Combes F., Deffayet C., Fort B., eds, EAS Publications Series Vol. 20, EAS Publications Series. pp 161--166 ( @eprint arXiv astro-ph/0511121 ), @doi 10.1051/eas:2006064

    2006 arXiv

  91. [104]

    M., Allam S

    Kubo J. M., Allam S. S., Annis J., Buckley-Geer E. J., Diehl H. T., Kubik D., Lin H., Tucker D., 2009, @doi [ ] 10.1088/0004-637X/696/1/L61 , https://ui.adsabs.harvard.edu/abs/2009ApJ...696L..61K 696, L61

    2009 doi

  92. [105]

    M., et al., 2010, @doi [ ] 10.1088/2041-8205/724/2/L137 , http://adsabs.harvard.edu/abs/2010ApJ...724L.137K 724, L137

    Kubo J. M., et al., 2010, @doi [ ] 10.1088/2041-8205/724/2/L137 , http://adsabs.harvard.edu/abs/2010ApJ...724L.137K 724, L137

    2010 doi

  93. [106]

    Kuijken K., et al., 2019, @doi [ ] 10.1051/0004-6361/201834918 , https://ui.adsabs.harvard.edu/abs/2019A&A...625A...2K 625, A2

    2019 doi

  94. [107]

    H., et al., 2022, @doi [ ] 10.3847/1538-4357/ac9186 , https://ui.adsabs.harvard.edu/abs/2022ApJ...940....9L 940, 9

    Laseter I. H., et al., 2022, @doi [ ] 10.3847/1538-4357/ac9186 , https://ui.adsabs.harvard.edu/abs/2022ApJ...940....9L 940, 9

    2022 doi

  95. [108]

    Le F \`e vre O., et al., 2013, @doi [ ] 10.1051/0004-6361/201322179 , https://ui.adsabs.harvard.edu/abs/2013A&A...559A..14L 559, A14

    2013 doi

  96. [109]

    Leaf K., Melia F., 2018, @doi [ ] 10.1093/mnras/sty1365 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.478.5104L 478, 5104

    2018 doi

  97. [110]

    Lemon C., et al., 2024, @doi [ ] 10.1007/s11214-024-01042-9 , https://ui.adsabs.harvard.edu/abs/2024SSRv..220...23L 220, 23

    2024 doi

  98. [111]

    arXiv:1910.13970

    Lewis A., 2019, @doi [arXiv e-prints] 10.48550/arXiv.1910.13970 , https://ui.adsabs.harvard.edu/abs/2019arXiv191013970L p. arXiv:1910.13970

    2019 doi arXiv

  99. [112]

    Lex A., Gehlenborg N., Strobelt H., Vuillemot R., Pfister H., 2014, @doi [IEEE Transactions on Visualization and Computer Graphics] 10.1109/TVCG.2014.2346248 , 20, 1983

    2014

  100. [113]

    Li R., et al., 2021, @doi [ ] 10.3847/1538-4357/ac2df0 , https://ui.adsabs.harvard.edu/abs/2021ApJ...923...16L 923, 16

    2021 doi

  101. [114]

    Lidman C., et al., 2020, @doi [ ] 10.1093/mnras/staa1341 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.496...19L 496, 19

    2020 doi

  102. [115]

    J., et al., 2007, @doi [ ] 10.1086/516589 , https://ui.adsabs.harvard.edu/abs/2007ApJS..172...70L 172, 70

    Lilly S. J., et al., 2007, @doi [ ] 10.1086/516589 , https://ui.adsabs.harvard.edu/abs/2007ApJS..172...70L 172, 70

    2007 doi

  103. [116]

    Lin H., et al., 2009, @doi [ ] 10.1088/0004-637X/699/2/1242 , https://ui.adsabs.harvard.edu/abs/2009ApJ...699.1242L 699, 1242

    2009 doi

  104. [117]

    J., Driver S

    Liske J., Lemon D. J., Driver S. P., Cross N. J. G., Couch W. J., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06826.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.344..307L 344, 307

    2003

  105. [118]

    Liu T., Liao K., 2024, @doi [ ] 10.1093/mnras/stae119 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.1354L 528, 1354

    2024 doi

  106. [119]

    Liu X.-H., Li Z.-H., Qi J.-Z., Zhang X., 2022, @doi [ ] 10.3847/1538-4357/ac4c3b , https://ui.adsabs.harvard.edu/abs/2022ApJ...927...28L 927, 28

    2022 doi

  107. [120]

    Lopes P. A. A., de Carvalho R. R., Gal R. R., Djorgovski S. G., Odewahn S. C., Mahabal A. A., Brunner R. J., 2004, @doi [ ] 10.1086/423038 , https://ui.adsabs.harvard.edu/abs/2004AJ....128.1017L 128, 1017

    2004 doi

  108. [121]

    R., Fekete G., Hogg D

    Lupton R., Blanton M. R., Fekete G., Hogg D. W., O'Mullane W., Szalay A., Wherry N., 2004, @doi [ ] 10.1086/382245 , https://ui.adsabs.harvard.edu/abs/2004PASP..116..133L 116, 133

    2004 doi

  109. [122]

    J., Newman J

    Matthews D. J., Newman J. A., Coil A. L., Cooper M. C., Gwyn S. D. J., 2013, @doi [ ] 10.1088/0067-0049/204/2/21 , https://ui.adsabs.harvard.edu/abs/2013ApJS..204...21M 204, 21

    2013 doi

  110. [123]

    pp 51--56

    McKinney W., et al., 2010, in Proceedings of the 9th Python in Science Conference. pp 51--56

    2010

  111. [124]

    R., Furlanetto C., Chies-Santos A

    Melo-Carneiro C. R., Furlanetto C., Chies-Santos A. L., 2023, @doi [ ] 10.1093/mnras/stad162 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.1613M 520, 1613

    2023 doi

  112. [125]

    Menanteau F., et al., 2010, @doi [ ] 10.1088/0067-0049/191/2/340 , https://ui.adsabs.harvard.edu/abs/2010ApJS..191..340M 191, 340

    2010 doi

  113. [126]

    B., et al., 2019, @doi [ ] 10.1051/0004-6361/201832797 , https://ui.adsabs.harvard.edu/abs/2019A&A...625A.119M 625, A119

    Metcalf R. B., et al., 2019, @doi [ ] 10.1051/0004-6361/201832797 , https://ui.adsabs.harvard.edu/abs/2019A&A...625A.119M 625, A119

    2019 doi

  114. [127]

    Millon et al., 2020, @doi [A&A] 10.1051/0004-6361/201937351 , 639, A101

    2020 doi

  115. [128]

    M \"o ller A., et al., 2021, @doi [ ] 10.1093/mnras/staa3602 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.3272M 501, 3272

    2021 doi

  116. [129]

    pp 202--203

    Moraes B., et al., 2014, in Revista Mexicana de Astronomia y Astrofisica Conference Series. pp 202--203

    2014

  117. [130]

    More A., Cabanac R., More S., Alard C., Limousin M., Kneib J.-P., Gavazzi R., Motta V., 2012, @doi [ ] 10.1088/0004-637X/749/1/38 , http://adsabs.harvard.edu/abs/2012ApJ...749...38M 749, 38

    2012 doi

  118. [131]

    More A., et al., 2016, @doi [MNRAS] 10.1093/mnras/stw2924 , 465, 2411

    2016 doi

  119. [132]

    Moustakas L., 2012, The Master Lens Database and The Orphan Lenses Project, HST Proposal

    2012

  120. [133]

    Murray A., et al., 2023, python-pillow/Pillow: 10.0.1, @doi 10.5281/zenodo.8349181 , https://doi.org/10.5281/zenodo.8349181

    2023 doi

  121. [134]

    C., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2502.09802 , https://ui.adsabs.harvard.edu/abs/2025arXiv250209802N p

    Nagam B. C., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2502.09802 , https://ui.adsabs.harvard.edu/abs/2025arXiv250209802N p. arXiv:2502.09802

    2025 doi

  122. [135]

    Nalepa M., till m 2023, pandarallel, https://nalepae.github.io/pandarallel/

    2023

  123. [136]

    R., et al., 2020, @doi [ ] 10.1093/mnras/staa2409 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.498.5704N 498, 5704

    Napolitano N. R., et al., 2020, @doi [ ] 10.1093/mnras/staa2409 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.498.5704N 498, 5704

    2020 doi

  124. [137]

    Negrello M., et al., 2017, @doi [ ] 10.1093/mnras/stw2911 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.3558N 465, 3558

    2017 doi

  125. [138]

    W., Dye S., Massey R

    Nightingale J. W., Dye S., Massey R. J., 2018, @doi [ ] 10.1093/mnras/sty1264 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.478.4738N 478, 4738

    2018 doi

  126. [139]

    W., Massey R

    Nightingale J. W., Massey R. J., Harvey D. R., Cooper A. P., Etherington A., Tam S.-I., Hayes R. G., 2019, @doi [MNRAS] 10.1093/mnras/stz2220 , 489, 2049

    2019 doi

  127. [140]

    W., et al., 2024, @doi [ ] 10.1093/mnras/stad3694 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.52710480N 527, 10480

    Nightingale J. W., et al., 2024, @doi [ ] 10.1093/mnras/stad3694 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.52710480N 527, 10480

    2024 doi

  128. [141]

    Nord B., et al., 2020, @doi [ ] 10.1093/mnras/staa200 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.1308N 494, 1308

    2020 doi

  129. [142]

    H., et al., 2021, @doi [arXiv e-prints] 10.3847/1538-4365/ac470b , https://ui.adsabs.harvard.edu/abs/2021arXiv211002418O p

    O'Donnell J. H., et al., 2021, @doi [arXiv e-prints] 10.3847/1538-4365/ac470b , https://ui.adsabs.harvard.edu/abs/2021arXiv211002418O p. arXiv:2110.02418

    2021

  130. [143]

    Ochsenbein F., Bauer P., Marcout J., 2000, @doi [ ] 10.1051/aas:2000169 , https://ui.adsabs.harvard.edu/abs/2000A&AS..143...23O 143, 23

    2000 doi

  131. [144]

    D., 2008, @doi [Monthly Notices of the Royal Astronomical Society] 10.1111/j.1365-2966.2008.14154.x , 392, 930

    Oguri M., Blandford R. D., 2008, @doi [Monthly Notices of the Royal Astronomical Society] 10.1111/j.1365-2966.2008.14154.x , 392, 930

    2008

  132. [145]

    Oller S., 2023, parmap, https://github.com/zeehio/parmap

    2023

  133. [146]

    F., et al., 2007, @doi [ ] 10.1051/0004-6361:20066088 , https://ui.adsabs.harvard.edu/abs/2007A&A...461...81O 461, 81

    Olsen L. F., et al., 2007, @doi [ ] 10.1051/0004-6361:20066088 , https://ui.adsabs.harvard.edu/abs/2007A&A...461...81O 461, 81

    2007 doi

  134. [147]

    S., Courbin F., Faure C., Kokotanekova R., Meylan G., 2014, @doi [ ] 10.1093/mnras/stu179 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.439.3392P 439, 3392

    Pawase R. S., Courbin F., Faure C., Kokotanekova R., Meylan G., 2014, @doi [ ] 10.1093/mnras/stu179 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.439.3392P 439, 3392

    2014 doi

  135. [148]

    E., et al., 2019, @doi [ ] 10.1093/mnras/stz189 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.3879P 484, 3879

    Petrillo C. E., et al., 2019, @doi [ ] 10.1093/mnras/stz189 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.3879P 484, 3879

    2019 doi

  136. [149]

    Pierre M., et al., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10886.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.372..591P 372, 591

    2006

  137. [150]

    Planck Collaboration et al., 2020, @doi [ ] 10.1051/0004-6361/201833910 , https://ui.adsabs.harvard.edu/abs/2020A&A...641A...6P 641, A6

    2020 doi

  138. [151]

    G., 2004, @doi [ ] 10.1051/0004-6361:20035818 , https://ui.adsabs.harvard.edu/abs/2004A&A...423..449P 423, 449

    Popesso P., B \"o hringer H., Brinkmann J., Voges W., York D. G., 2004, @doi [ ] 10.1051/0004-6361:20035818 , https://ui.adsabs.harvard.edu/abs/2004A&A...423..449P 423, 449

    2004 doi

  139. [153]

    Rojas K., et al., 2022, @doi [ ] 10.1051/0004-6361/202142119 , https://ui.adsabs.harvard.edu/abs/2022A&A...668A..73R 668, A73

    2022 doi

  140. [154]

    J., Treu T., Ellis R

    Sand D. J., Treu T., Ellis R. S., Smith G. P., 2005, @doi [ ] 10.1086/430298 , http://adsabs.harvard.edu/abs/2005ApJ...627...32S 627, 32

    2005 doi

  141. [155]

    Saunders W., et al., 2000, @doi [ ] 10.1046/j.1365-8711.2000.03528.x , https://ui.adsabs.harvard.edu/abs/2000MNRAS.317...55S 317, 55

    2000

  142. [156]

    arXiv:2110.11972

    Savary E., et al., 2021, @doi [arXiv e-prints] 10.48550/arXiv.2110.11972 , https://ui.adsabs.harvard.edu/abs/2021arXiv211011972S p. arXiv:2110.11972

    2021 doi arXiv

  143. [157]

    E., 1992, Gravitational Lenses

    Schneider P., Ehlers J., Falco E. E., 1992, Gravitational Lenses. Astronomy and Astrophysics Library, Springer-Verlag, @doi 10.1007/978-3-662-03758-4

    1992 doi

  144. [158]

    S., Rappaport S

    Schwab J., Bolton A. S., Rappaport S. A., 2010, @doi [ ] 10.1088/0004-637X/708/1/750 , https://ui.adsabs.harvard.edu/abs/2010ApJ...708..750S 708, 750

    2010 doi

  145. [159]

    D., et al., 2010, @doi [ ] 10.1051/0004-6361/201014330 , https://ui.adsabs.harvard.edu/abs/2010A&A...513L..10S 513, L10

    Schwope A. D., et al., 2010, @doi [ ] 10.1051/0004-6361/201014330 , https://ui.adsabs.harvard.edu/abs/2010A&A...513L..10S 513, L10

    2010 doi

  146. [160]

    Scodeggio M., et al., 2018, @doi [ ] 10.1051/0004-6361/201630114 , https://ui.adsabs.harvard.edu/abs/2018A&A...609A..84S 609, A84

    2018 doi

  147. [161]

    J., et al., 2019, @doi [ ] 10.1093/mnras/sty3397 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.5649S 483, 5649

    Shajib A. J., et al., 2019, @doi [ ] 10.1093/mnras/sty3397 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.5649S 483, 5649

    2019 doi

  148. [162]

    J., et al., 2020, @doi [ ] 10.1093/mnras/staa828 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.6072S 494, 6072

    Shajib A. J., et al., 2020, @doi [ ] 10.1093/mnras/staa828 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.6072S 494, 6072

    2020 doi

  149. [163]

    Shajib et al., 2023, @doi [A&A] 10.1051/0004-6361/202345878 , 673, A9

    2023 doi

  150. [164]

    A., Landy S

    Shectman S. A., Landy S. D., Oemler A., Tucker D. L., Lin H., Kirshner R. P., Schechter P. L., 1996, @doi [ ] 10.1086/177858 , https://ui.adsabs.harvard.edu/abs/1996ApJ...470..172S 470, 172

    1996 doi

  151. [165]

    Shu Y., et al., 2016, @doi [ ] 10.3847/0004-637X/824/2/86 , https://ui.adsabs.harvard.edu/abs/2016ApJ...824...86S 824, 86

    2016 doi

  152. [166]

    Shu Y., et al., 2017, @doi [ ] 10.3847/1538-4357/aa9794 , https://ui.adsabs.harvard.edu/abs/2017ApJ...851...48S 851, 48

    2017 doi

  153. [167]

    H., Taubenberger S., Inoue K

    Shu Y., Ca \ n ameras R., Schuldt S., Suyu S. H., Taubenberger S., Inoue K. T., Jaelani A. T., 2022, @doi [ ] 10.1051/0004-6361/202243203 , https://ui.adsabs.harvard.edu/abs/2022A&A...662A...4S 662, A4

    2022 doi

  154. [168]

    H., Treu T., Marshall P

    Sonnenfeld A., Gavazzi R., Suyu S. H., Treu T., Marshall P. J., 2013a, @doi [ ] 10.1088/0004-637X/777/2/97 , https://ui.adsabs.harvard.edu/abs/2013ApJ...777...97S 777, 97

    doi

  155. [169]

    H., Marshall P

    Sonnenfeld A., Treu T., Gavazzi R., Suyu S. H., Marshall P. J., Auger M. W., Nipoti C., 2013b, @doi [ ] 10.1088/0004-637X/777/2/98 , https://ui.adsabs.harvard.edu/abs/2013ApJ...777...98S 777, 98

    doi

  156. [170]

    Sonnenfeld A., et al., 2018, @doi [ ] 10.1093/pasj/psx062 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S..29S 70, S29

    2018 doi

  157. [171]

    T., Chan J., More A., Suyu S

    Sonnenfeld A., Jaelani A. T., Chan J., More A., Suyu S. H., Wong K. C., Oguri M., Lee C.-H., 2019, @doi [ ] 10.1051/0004-6361/201935743 , https://ui.adsabs.harvard.edu/abs/2019A&A...630A..71S 630, A71

    2019 doi

  158. [172]

    Sonnenfeld A., et al., 2020, @doi [ ] 10.1051/0004-6361/202038067 , https://ui.adsabs.harvard.edu/abs/2020A&A...642A.148S 642, A148

    2020 doi

  159. [173]

    P., et al., 2013, @doi [ ] 10.1093/mnras/stt1624 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.436.1040S 436, 1040

    Stark D. P., et al., 2013, @doi [ ] 10.1093/mnras/stt1624 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.436.1040S 436, 1040

    2013 doi

  160. [174]

    arXiv:2110.00023

    Stein G., Blaum J., Harrington P., Medan T., Lukic Z., 2021, @doi [arXiv e-prints] 10.3847/1538-4357/ac6d63 , https://ui.adsabs.harvard.edu/abs/2021arXiv211000023S p. arXiv:2110.00023

    2021 arXiv

  161. [175]

    arXiv:2206.02764

    Storfer C., et al., 2022, @doi [arXiv e-prints] 10.48550/arXiv.2206.02764 , https://ui.adsabs.harvard.edu/abs/2022arXiv220602764S p. arXiv:2206.02764

    2022 doi arXiv

  162. [176]

    H., et al., 2017, @doi [ ] 10.1093/mnras/stx483 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.468.2590S 468, 2590

    Suyu S. H., et al., 2017, @doi [ ] 10.1093/mnras/stx483 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.468.2590S 468, 2590

    2017 doi

  163. [177]

    F., Tu H., Fort B., Gavazzi R., 2010, @doi [ ] 10.1051/0004-6361/200913977 , https://ui.adsabs.harvard.edu/abs/2010A&A...517A..25S 517, A25

    Sygnet J. F., Tu H., Fort B., Gavazzi R., 2010, @doi [ ] 10.1051/0004-6361/200913977 , https://ui.adsabs.harvard.edu/abs/2010A&A...517A..25S 517, A25

    2010 doi

  164. [178]

    Szymański P., 2022, pqdm, https://github.com/niedakh/pqdm

    2022

  165. [179]

    S., et al., 2018, @doi [ ] 10.1093/mnras/sty653 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477..195T 477, 195

    Talbot M. S., et al., 2018, @doi [ ] 10.1093/mnras/sty653 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477..195T 477, 195

    2018 doi

  166. [180]

    S., Brownstein J

    Talbot M. S., Brownstein J. R., Dawson K. S., Kneib J.-P., Bautista J., 2021, @doi [ ] 10.1093/mnras/stab267 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.4617T 502, 4617

    2021 doi

  167. [181]

    S., Brownstein J

    Talbot M. S., Brownstein J. R., Neumann J., Thomas D., Maraston C., Drory N., 2022, @doi [ ] 10.1093/mnras/stac1736 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.515.4953T 515, 4953

    2022 doi

  168. [182]

    Tanaka M., et al., 2016, @doi [ ] 10.3847/2041-8205/826/2/L19 , https://ui.adsabs.harvard.edu/abs/2016ApJ...826L..19T 826, L19

    2016 doi

  169. [183]

    D., 2020, Joblib: running Python functions as pipeline jobs, https://joblib.readthedocs.io/

    Team J. D., 2020, Joblib: running Python functions as pipeline jobs, https://joblib.readthedocs.io/

    2020

  170. [184]

    D., Rodrigues D

    Toniato J. D., Rodrigues D. C., 2021, @doi [ ] 10.1103/PhysRevD.104.044020 , https://ui.adsabs.harvard.edu/abs/2021PhRvD.104d4020T 104, 044020

    2021 doi

  171. [185]

    Tretyakov K., 2023, matplotlib-venn, https://github.com/konstantint/matplotlib-venn

    2023

  172. [186]

    A., Auger M

    Treu T., Dutton A. A., Auger M. W., Marshall P. J., Bolton A. S., Brewer B. J., Koo D. C., Koopmans L. V. E., 2011, @doi [ ] 10.1111/j.1365-2966.2011.19378.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.417.1601T 417, 1601

    2011

  173. [187]

    R., 2009, @doi [MNRAS] 10.1111/j.1365-2966.2009.15167.x , 398, 607

    Van De Ven G., Mandelbaum R., Keeton C. R., 2009, @doi [MNRAS] 10.1111/j.1365-2966.2009.15167.x , 398, 607

    2009

  174. [188]

    L., 2009, Python 3 Reference Manual

    Van Rossum G., Drake F. L., 2009, Python 3 Reference Manual. CreateSpace, Scotts Valley, CA

    2009

  175. [189]

    Verma A., Collett T., Smith G. P., Strong Lensing Science Collaboration the DESC Strong Lensing Science Working Group 2019, @doi [arXiv e-prints] 10.48550/arXiv.1902.05141 , https://ui.adsabs.harvard.edu/abs/2019arXiv190205141V p. arXiv:1902.05141

    2019 doi arXiv

  176. [190]

    Virtanen P., et al., 2020, @doi [Nature Methods] 10.1038/s41592-019-0686-2 , https://rdcu.be/b08Wh 17, 261

    2020 doi

  177. [191]

    Vujeva L., Lo R. K. L., Ezquiaga J. M., Chan J. C. L., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2406.04398 , https://ui.adsabs.harvard.edu/abs/2024arXiv240604398V p. arXiv:2406.04398

    2024 doi arXiv

  178. [192]

    O., Darragh-Ford E., Marshall P

    Wagner-Carena S., Aalbers J., Birrer S., Nadler E. O., Darragh-Ford E., Marshall P. J., Wechsler R. H., 2023, @doi [ApJ] 10.3847/1538-4357/aca525 , 942, 75

    2023 doi

  179. [193]

    J., Iovino A., Hewett P

    Warren S. J., Iovino A., Hewett P. C., Shaver P. A., 1998, @doi [ ] 10.1046/j.1365-8711.1998.01881.x , https://ui.adsabs.harvard.edu/abs/1998MNRAS.299.1215W 299, 1215

    1998

  180. [194]

    Wei J.-J., Chen Y., Cao S., Wu X.-F., 2022, @doi [ ] 10.3847/2041-8213/ac551e , https://ui.adsabs.harvard.edu/abs/2022ApJ...927L...1W 927, L1

    2022 doi

  181. [195]

    Wen Z.-L., Han J.-L., Jiang Y.-Y., 2011, @doi [Research in Astronomy and Astrophysics] 10.1088/1674-4527/11/10/007 , http://adsabs.harvard.edu/abs/2011RAA....11.1185W 11, 1185

    2011 doi

  182. [196]

    C., et al., 2018, @doi [ ] 10.3847/1538-4357/aae381 , https://ui.adsabs.harvard.edu/abs/2018ApJ...867..107W 867, 107

    Wong K. C., et al., 2018, @doi [ ] 10.3847/1538-4357/aae381 , https://ui.adsabs.harvard.edu/abs/2018ApJ...867..107W 867, 107

    2018 doi

  183. [197]

    C., et al., 2020, @doi [ ] 10.1093/mnras/stz3094 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.498.1420W 498, 1420

    Wong K. C., et al., 2020, @doi [ ] 10.1093/mnras/stz3094 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.498.1420W 498, 1420

    2020 doi

  184. [198]

    C., Chan J

    Wong K. C., Chan J. H. H., Chao D. C. Y., Jaelani A. T., Kayo I., Lee C.-H., More A., Oguri M., 2022, @doi [ ] 10.1093/pasj/psac065 , https://ui.adsabs.harvard.edu/abs/2022PASJ...74.1209W 74, 1209

    2022 doi

  185. [199]

    Yang T., Birrer S., Hu B., 2020, @doi [ ] 10.1093/mnrasl/slaa107 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.497L..56Y 497, L56

    2020 doi

  186. [200]

    Yee H. K. C., et al., 2000, @doi [ ] 10.1086/313426 , https://ui.adsabs.harvard.edu/abs/2000ApJS..129..475Y 129, 475

    2000 doi

  187. [201]

    A., et al., 2023, @doi [ ] 10.3847/1538-4357/ace4ba , https://ui.adsabs.harvard.edu/abs/2023ApJ...954...68Z 954, 68

    Zaborowski E. A., et al., 2023, @doi [ ] 10.3847/1538-4357/ace4ba , https://ui.adsabs.harvard.edu/abs/2023ApJ...954...68Z 954, 68

    2023 doi

  188. [202]

    Zitrin A., Broadhurst T., 2009, @doi [ApJ] 10.1088/0004-637X/703/2/L132 , 703, L132

    2009 doi

  189. [203]

    Zonca A., Singer L., Lenz D., Reinecke M., Rosset C., Hivon E., Gorski K., 2020, healpy: Python wrapper for HEALPix, Astrophysics Source Code Library, record ascl:2008.022 ( @eprint ascl 2008.022 )

    2020

  190. [204]

    da Costa-Luis C., et al., 2023, tqdm: A fast, Extensible Progress Bar for Python and CLI, @doi 10.5281/zenodo.8233425 , https://doi.org/10.5281/zenodo.8233425

    2023 doi

  191. [205]

    N., et al., 1998, @doi [ ] 10.1086/300410 , https://ui.adsabs.harvard.edu/abs/1998AJ....116....1D 116, 1

    da Costa L. N., et al., 1998, @doi [ ] 10.1086/300410 , https://ui.adsabs.harvard.edu/abs/1998AJ....116....1D 116, 1

    1998 doi

  192. [206]

    de Jong J. T. A., Verdoes Kleijn G. A., Kuijken K. H., Valentijn E. A., 2013, @doi [Experimental Astronomy] 10.1007/s10686-012-9306-1 , https://ui.adsabs.harvard.edu/abs/2013ExA....35...25D 35, 25

    2013 doi

  193. [207]

    de Jong J. T. A., et al., 2017, @doi [ ] 10.1051/0004-6361/201730747 , https://ui.adsabs.harvard.edu/abs/2017A&A...604A.134D 604, A134

    2017 doi

  194. [208]

    van Breukelen C., et al., 2006, @doi [ ] 10.1111/j.1745-3933.2006.00236.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.373L..26V 373, L26

    2006

  195. [209]

    van Dokkum P., Brammer G., Wang B., Leja J., Conroy C., 2023, @doi [Nature Astronomy] 10.1038/s41550-023-02103-9 , https://ui.adsabs.harvard.edu/abs/2023NatAs.tmp....7V

    2023 doi

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.