Pith. sign in

REVIEW 3 major objections 5 minor 247 references

Identifying and Distinguishing Quenching Galaxies with Spatially Resolved Star Formation in the Hubble Frontier Fields

T0 review · 3 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

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

Pith's one-line read The paper claims it can count the two main galaxy quenching pathways—inside-out and outside-in—directly from the spatial pattern of star formation, finding 129 inside-out and 70 outside-in among 1,437 Frontier Fields galaxies.

desk verdict First real-galaxy census of inside-out vs outside-in quenching from a TNG-trained classifier, but the simulation-to-observation label transfer is unvalidated, so the counts and trends are conditional. read the letter →

arxiv 2607.21560 v1 pith:UO4HUJIS submitted 2026-07-23 astro-ph.GA

Cameron Lawlor-Forsyth , Michael L. Balogh , Sean L. McGee , Gregory H. Rudnick This is my paper
classification astro-ph.GA
keywords galaxyquenchinginside-outoutside-inmorphologicalmetricsspatiallyresolvedSEDfittingFrontierFieldsclustersstar-formingmainsequence
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 tries to establish that the two main ways galaxies stop forming stars—quenching that begins in the center and spreads outward, and quenching that begins at the edge and moves inward—can be recognized and counted in real galaxies using only deep, spatially resolved imaging, and that the two modes have distinct mass and environment fingerprints. The authors analyze 1,437 galaxies with stellar mass at least 10^8 solar masses and star formation rate at least 10^-3 solar masses per year in the Frontier Fields, fitting each galaxy's spectral energy distribution in 20 radial annuli to obtain star-formation morphology, then use a k-nearest-neighbors classifier trained on simulated galaxies to label each system as star-forming, inside-out, or outside-in. They report 129 inside-out and 70 outside-in candidates; inside-out galaxies are 0.8 dex more massive, are mostly found in clusters (106 of 129), and their fraction of the non-quenched population climbs steeply with mass, reaching roughly 30% at high mass in clusters. Outside-in galaxies are mostly field galaxies and their fraction is roughly constant at under 10%. If correct, this is the first observational census of the two quenching pathways, and it shows that in clusters the inside-out mode—traditionally associated with massive field centrals and AGN feedback—is the dominant pathway, implying that the real Universe quenches in a more complex way than the simulations used for the training labels.

What carries the argument

The argument is carried by four morphological metrics of star-formation placement—the concentration of star formation in the inner kiloparsec, the ratio of the star-forming half-light radius to the stellar half-light radius, and inner/outer truncation radii where the specific star-formation profile drops sharply—together with distance from the star-forming main sequence. These five features are measured from 20-annulus spectral energy distribution fits and passed to a k-nearest-neighbors classifier (five neighbors, inverse-distance weighting) whose training set is roughly 69,000 simulated galaxies with known star-forming/inside-out/outside-in labels. The kNN transfer is the mechanism that co

What would settle it

Take the 199 classified galaxies and measure their specific star-formation radial profiles with an independent tracer—e.g., Hα or rest-optical integral-field spectroscopy from a near-infrared space telescope. If a majority of the 129 inside-out candidates do not show centrally suppressed star formation, or the 70 outside-in candidates do not show outer truncation, then the simulation-to-real label transfer is the broken link. A second direct test: rerun the classifier with the same observed metrics but with training labels from an independent simulation with weaker AGN feedback; if the inside-

Watch

Extended reading notes

Core claim

The paper's central discovery is an empirical census: in 1,437 non-quenched Frontier Fields galaxies (stellar mass ≥ 10^8 solar masses, star formation rate ≥ 10^-3 solar masses per year, redshifts 0.14–0.67), four morphological metrics measuring where star formation lives inside a galaxy identify 129 galaxies whose star-formation distribution matches the inside-out quenching signature and 70 matching the outside-in signature. The inside-out population is more massive by Δlog M* = 0.8 (+0.2, −0.1) dex and is cluster-dominated (106/129); the outside-in population is field-dominated (25/70 in clusters). In clusters the inside-out fraction rises from small values at low mass to roughly 30% of al

Load-bearing premise

The claim collapses if the simulated galaxies used as the training set, after being degraded into mock observations, carry inside-out/outside-in labels that do not correspond to the same physical quenching mechanisms at work in real Frontier Fields galaxies; the paper explicitly concedes in Section 5.3 that the simulation's AGN feedback and environmental gas removal may be stronger than in the real Universe.

Editorial extensions

If this is right

  • If the census is right, inside-out quenching is not confined to massive field centrals: it is also the dominant quenching mode for massive cluster galaxies, so environment does not uniquely select outside-in quenching.
  • The 0.8 dex mass gap between the two pathways makes stellar mass the primary axis on which quenching mode is decided, with cluster membership adding another ~0.8 dex of mass to both populations.
  • The mass-independence of the outside-in fraction (≲10% in clusters and field) implies that environment-driven outer-to-inner quenching affects only a minority of recently star-forming galaxies at these redshifts.
  • For the most massive inside-out galaxies the fraction grows with phase-space-inferred infall time, suggesting long cluster residence promotes this pathway—possibly by funneling gas onto a central active nucleus—while the lack of infall-time dependence for outside-in is consistent with a fast (~1–1.5 Gyr) quenching event.
  • The pipeline is designed to be applied to wide-area surveys now in planning, where the authors expect the same metrics to deliver quenching-pathway samples numbering in the thousands to millions.

Reading between the lines

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

  • Editorial inference: If the simulation's AGN feedback is as strong as the paper itself allows (Section 5.3), the 129-galaxy inside-out count may be an overestimate of the real population; re-running the classifier with a simulation with weaker feedback is a direct way to bracket this uncertainty.
  • Editorial inference: A simulation-free check is available: apply the same annulus metrics to galaxies with independent quenching signatures—such as jellyfish galaxies undergoing ram-pressure stripping or type-1 AGN hosts—and see whether they are labeled outside-in and inside-out respectively; this can be done with existing space-based imaging and spectroscopy.
  • Editorial inference: If the method scales as the authors argue, the inside-out-to-outside-in number ratio as a function of stellar mass, environment, and infall time becomes a summary statistic that next-generation cosmological simulations can be tuned to match.
  • Editorial inference: The cluster/field mass offset (0.8 dex) for both pathways may partly reflect the selection function of the Frontier Fields (lensed deeper, more massive cluster galaxies), so the environmental comparison should be re-examined in volume-limited surveys before adopting it as a physical result.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper applies the morphological quenching metrics developed in Papers I/II (C_SF, R_SF, R_inner, R_outer) to 1,437 Hubble Frontier Fields galaxies with M* >= 1e8 Msun and SFR >= 1e-3 Msun/yr, deriving annular SED fits with FAST++. A k-nearest-neighbor classifier trained on 69,493 TNG50 galaxies, using the four metrics plus ΔSFMS, labels 129 galaxies as inside-out and 70 as outside-in quenching candidates. The paper then reports that inside-out candidates are ~0.8 dex more massive than outside-in candidates, that inside-out candidates are predominantly in clusters (106/129) while outside-in candidates are mainly in the field (25/70 in clusters), that the cluster inside-out fraction rises with stellar mass to ~30% of the non-quenched population, and that outside-in fractions are roughly mass-independent. The final sections discuss literature comparisons, survey feasibility, and limitations.

Significance. If the classification transfer from TNG50 to observed galaxies is valid, this would be one of the first observational censuses of inside-out versus outside-in quenching pathways using spatially resolved star-formation morphology at z ~ 0.3, with a valuable cluster-versus-field contrast. The paper has real strengths: careful sample construction, detailed annular photometry and SED fitting, explicit treatment of bCGs and PSF matching, and a welcome transparency about limitations. The comparison with independent CANUCS/UNCOVER SED catalogs in Appendix D is a useful consistency check. However, the central scientific claims — the counts, the mass offset, and the cluster/field fractions — all depend on the unvalidated assumption that TNG50 morphological neighborhoods correspond to real quenching pathways. The manuscript itself acknowledges that the Figure 5 agreement is by construction and that TNG's AGN feedback and environmental gas removal may be too strong. These are load-bearing caveats rather than routine disclaimers, so the significance is conditional on additional validation.

major comments (3)
  1. [§3.4, §4, Figure 5] The kNN transfer is validated only circularly. Every observed galaxy's inside-out/outside-in label is inherited from the nearest TNG50 training galaxies in the same metric space, so the KS-test agreement in Figure 5 is unsurprising; the text concedes this ('by construction these distributions should be similar', Section 4). This is not an independent test of the label transfer. The headline numbers (129/70), the 0.8 dex mass offset, and the 106/129 cluster fraction are all downstream of this transfer. I ask for a concrete validation or sensitivity analysis: e.g., leave-one-out cross-validation on the TNG training set, a mock-recovery test where synthetic observed galaxies with known injected profiles are classified, training on an independent simulation, or a threshold-based direct classifier as a cross-check. Without one of these, the central demographic claims are not established.
  2. [§3.4, §4, Figures 8–9] The near-exclusive green-valley membership of the classified quenching candidates is admitted in Section 4 to be 'likely a consequence' of including ΔSFMS in the kNN feature set. But Figures 8 and 9 then compute fractions relative to the non-quenched and green-valley samples. If the classifier is strongly influenced by ΔSFMS, the mass and environment trends of the resulting fractions could be partly manufactured by the classifier itself rather than discovered in the data. The random-forest feature-importance statement (ΔSFMS importance 0.25) does not resolve this, because it measures predictive power in the TNG training set, not independence of the HFF classification. Please rerun the classification without ΔSFMS, or with ΔSFMS replaced by a balanced control variable, and report whether the counts, the 0.8 dex mass offset, and the cluster/field fractions survive.
  3. [§5.3, Paper I comparison] Section 5.3 states that TNG's AGN feedback and environmental gas removal may be too strong, citing literature. This matters directly: the paper's observed clustering of inside-out candidates (106/129 in clusters) reverses the Paper I simulation expectation that inside-out galaxies are predominantly field galaxies. The kNN transfer is therefore being asked to support a qualitatively new claim precisely in the regime where the simulation's physical fidelity is most doubtful. The comparison of metric distributions in Figure 5 cannot detect this bias, since the training and target samples are matched by construction. A concrete test is needed: for example, compare the resolved SFR/sSFR profiles of observed candidates with TNG profiles at matched M*, environment, and ΔSFMS; or retrain on TNG after suppressing/boosting AGN-driven central suppression and see whether the HFF classifications rema
minor comments (5)
  1. [§2.3, Figure 6 caption] The phrase 'numbering 682, excluding 556 kNN-classified star forming galaxies' is confusing. Clarify the bookkeeping: 1437 primary − 199 pathway candidates − 556 star forming = 682 unclassified non-quenched galaxies.
  2. [§3.4] State explicitly which distance metric is used for the kNN classifier (Euclidean, standardized Mahalanobis, etc.). The text mentions inverse-distance weighting and k=5 but not the metric.
  3. [§4, Figure 5] Because the KS p-values in Figure 5 are acknowledged to be non-independent, consider moving this figure to an appendix or clearly labeling it as a sanity check rather than a validation.
  4. [§5.1] The comparison with Nelson et al. (2021) concerns 0.7 < z < 1.5, which does not fully overlap with this paper's 0.14 < z < 0.67. A sentence noting the redshift offset and its possible effect on quenching stage would help.
  5. [Appendix D] The statement of 'substantial overlap' with CANUCS/UNCOVER is qualitative and no figure is shown. Adding a quantitative comparison (e.g., the fraction of each sample in the same ΔSFMS bins) would strengthen this useful cross-check.

Circularity Check

2 steps flagged · score 6.0 of 10

Two supporting results—the Figure 5 metric-distribution 'agreement' and the green-valley exclusivity of candidates—are substantially built into the kNN classifier by construction; the central 129/70 demographics are not definitionally forced but rest on an unvalidated simulation-to-observation label transfer.

  1. self definitional [Section 4, Figure 5 paragraph]
    "Of course, by construction these distributions should be similar, as in Section 3.4 we used the simulated galaxies as the training set, in order to classify the sample galaxies according to their morphological metrics."

    Each HFF galaxy's inside-out/outside-in label is inherited from its k nearest TNG50 galaxies in the same standardized four-metric space (Section 3.4). Therefore the metric distributions of the labeled HFF galaxies are pulled toward the TNG populations used as the training set, so the reported KS agreement (p = 0.54...0.99) is expected and cannot validate the transfer. The paper presents this as 'encouraging' evidence, but the agreement is guaranteed by the labeling rule rather than by physical fidelity.

  2. fitted input called prediction [Section 4, after Figure 7]
    "In both the field and cluster, inside-out and outside-in quenching galaxies are almost exclusively found in the green valley (also see Appendix D), which is likely a consequence of including the distance from the SFMS, ΔSFMS, in the kNN classification above."

    ΔSFMS is one of the five kNN features. Because TNG quenching labels preferentially populate low ΔSFMS, a target galaxy far from the green valley is unlikely to be assigned an inside-out/outside-in label. The exclusivity result is thus at least partly created by the classifier input, as the paper concedes ('likely a consequence'); reporting it as a finding (and using it as the parent population in Figure 8) is partially circular. The random-forest importance (0.25) shows metrics matter too, so the effect is partial, not total.

full rationale

The central demographic claims (129 inside-out, 70 outside-in; 0.8 dex mass offset; 106/129 in clusters) are not definitionally forced: they are conditional on the kNN label transfer, and the cluster/field asymmetry actually reverses the Paper I expectation that inside-out galaxies are field-dominated, so the demographics have independent content given the transfer. The transfer itself, however, is justified by citation to the same authors' Papers I/II, whose TNG50-derived labels are not independently validated against observed quenching mechanisms; §5.3 concedes that TNG AGN feedback and environmental gas removal may be too strong, so the simulated morphological neighborhoods may not correspond to real quenching pathways. That is a correctness risk rather than circularity. The genuinely circular elements are the two supporting results: Figure 5's distributional 'validation' is by the paper's own admission a by-construction artifact of training-set reuse, and the near-exclusive green-valley membership is 'likely a consequence' of including ΔSFMS as a classifier input. These two steps mean part of the claimed support and one secondary finding reduce to the classifier's inputs; hence a partial-circularity score of 6 rather than a total collapse of the derivation.

Assumptions & free parameters 7 free parameters · 7 assumptions · 0 invented entities

The central classification rests on a chain of domain assumptions: the SED SFH model, the TNG50-to-real transfer of pathway labels, the COSMOS-Web SFMS calibration, and the phase-space infall-time mapping. The free parameters most directly affecting the headline numbers are the kNN hyperparameters, the Delta_SFMS green-valley threshold, the truncation-radius thresholds, and the cluster membership boundary. No new physical entities are introduced; 'inside-out' and 'outside-in' are pre-existing pathway labels inherited from Papers I/II.

free parameters (7)
  • SFMS ridge-line slope alpha and intercept beta = not quoted; fitted to COSMOS-Web galaxies with M* <= 10^10.1 and extrapolated above
    Delta_SFMS is a kNN feature and the green-valley threshold depends on this external fit (Section 3.3).
  • Green valley threshold Delta_SFMS = -0.5 dex = -0.5 dex
    Adopted from Pillepich et al. (2019); defines the parent population for fraction trends and is also a kNN input (Section 3.3 and 3.4).
  • kNN hyperparameters k=5, inverse-distance weighting = k = 5
    Brought from Paper I/II; the 129/70 counts and all downstream trends depend on this choice (Section 3.4).
  • Truncation metric thresholds: log sSFR <= -10.5 and |d log sSFR/dr| >= 1 = -10.5 yr^-1, +/-1
    Equations (4)-(5) define R_inner and R_outer and are carried from Paper I without recalibration on real galaxies.
  • SFR averaging timescale = 100 Myr = 100 Myr
    All SFRs, including the primary-sample cut SFR >= 10^-3 Msun/yr and Delta_SFMS, use SFR averaged over 100 Myr (Section 3.2).
  • Cluster membership boundary R_2D <= 0.5 R_200 and |V_LoS| <= 3.5 sigma_LoS = 0.5, 3.5
    Cluster versus field assignment for both populations depends on this threshold (Section 2.3.2).
  • H-band SNR threshold >= 3 in all 20 annuli out to 5 R_e = SNR >= 3
    Primary-sample selection excludes ~45% of candidates and may bias against low-surface-brightness outskirts (Sections 2.3 and 5.3).
assumptions (7)
  • domain assumption The hybrid delayed-tau SFH with a multiplicative R in the final 100 Myr, BC03 SSPs, Chabrier IMF, and Calzetti dust describe the stellar populations in every annulus well enough for reliable M* and SFR radial profiles.
    Section 3.2; if the SFH parameterization is wrong, the radial profiles and all derived metrics and classifications are biased.
  • domain assumption TNG50 internal inside-out/outside-in labels transfer to real HFF galaxies after mock-observation degradation.
    Section 3.4 uses 69,493 TNG galaxies as training labels; Section 5.3 concedes TNG AGN feedback and environmental gas removal may be too strong. This is the load-bearing transfer assumption.
  • domain assumption FAST++ integrated and annular fits recover M* and SFR with the accuracy found in the mock tests of Paper II when applied to real HFF data.
    Section 3.2; internal consistency with integrated fits and qualitative agreement with CANUCS/UNCOVER (Appendix D) are not an external calibration on real galaxies.
  • domain assumption COSMOS-Web CIGALE-derived SFRs and M* define an unbiased star-forming main sequence for 0.25 < z < 0.6 that can be applied to the HFF sample.
    Section 3.3 and Appendix B; the fit uses a single redshift range and is linearly extrapolated above M* = 10^10.1 Msun.
  • domain assumption The Dou & Yu (2025) / Lawlor-Forsyth (2026) phase-space-to-infall-time mapping, calibrated on TNG300/TNG-Cluster, applies to HFF cluster galaxies at 0.14 < z < 0.67.
    Section 2.3.2; infall-time results in Figure 9 and claims about ancient infall times depend on this mapping.
  • domain assumption The SFR >= 10^-3 Msun/yr cut cleanly separates 'quenched' from 'non-quenched primary' galaxies without biasing quenching-pathway fractions.
    Sections 2.3 and 3.2; the 502 excluded galaxies may be at a late stage of inside-out/outside-in quenching with low integrated SFR, so the reported fractions are conditional on the primary sample.
  • standard math kNN, KS tests, bootstrap resampling, and binomial errors are adequate statistical tools for the sample sizes used here.
    Sections 3.4 and 4; the paper does not correct for multiple comparisons or propagate classifier uncertainty into the statistical errors.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Identifying and Distinguishing Quenching Galaxies with Spatially Resolved Star Formation in the Hubble Frontier Fields." pith.science (2026). https://pith.science/paper/UO4HUJIS

@misc{pith2026260721560,
  author       = {Pith},
  title        = {Pith review of: Identifying and Distinguishing Quenching Galaxies with Spatially Resolved Star Formation in the Hubble Frontier Fields},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UO4HUJIS}},
  note         = {Machine review of arXiv:2607.21560}
}
abstract

We investigate the nature and prevalence of different quenching signatures for 1437 galaxies with $M_{*} \geqslant 10^{8}~\mathrm{M}_{\odot}$ and $\text{SFR} \geqslant 10^{-3}~M_{\odot}~\text{yr}^{-1}$ in the Hubble Frontier Fields through spatially resolved spectral energy distribution fitting with \texttt{FAST++}. We use the morphological metrics previously presented in our series of investigations to quantify the distribution of star formation, and use a $k$-nearest neighbors algorithm to classify quenching galaxies into different quenching pathways, including an inside-out pathway and an outside-in pathway. We find 129 galaxies have morphologies consistent with an inside-out quenching pathway, and 70 are consistent with an outside-in pathway. Inside-out quenching galaxies are $0.8^{+0.2}_{-0.1}~\text{dex}$ more massive compared to outside-in quenching galaxies, where both populations are more massive in clusters than the field, by $0.8^{+0.3}_{-0.1}~\text{dex}$. Inside-out quenching galaxies are found more often in clusters (106/129), compared to outside-in quenching galaxies (25/70). In clusters, the fraction of inside-out quenching galaxies strongly increases with mass, representing ${\sim}$30% of the non-quenched galaxy population at high masses. A milder evolution is seen in the field. The fraction of outside-in quenching galaxies is independent of mass, representing ${\lesssim}$10% of the non-quenched population, in both the cluster and field. We find no strong dependence for the fraction of any population with estimated infall time, except massive inside-out quenching galaxies, which increase in fraction with increasing infall time.

Figures

Figures reproduced from arXiv: 2607.21560 by the authors.

Figure 1
Figure 1. HST filter transmission curves for the Frontier Fields bands across all clusters and parallel fields. Not every band is available for every pointing (i.e. cluster or parallel field). See Appendix A for full details regarding which filters are available per pointing. delayed exponential (or “tau”) model for their star formation history along with a fixed solar metallicity (𝑍 = 0.02). The imaging available from Shiple… view at source ↗
Figure 2
Figure 2. Example cutouts for galaxy ID 5809 in MACS J0717 (𝑧spec = 0.546, 𝑀∗ = 1010.44 𝑀⊙, SFR = 0.36 𝑀⊙ yr−1 ). In each cutout, pixels with negative values are shown in a log-grayscale, while positive pixels are shown in color with a log scale. Each cutout uses a per-cutout scaling, meaning a universal scaling for all cutouts has not been applied. Cutouts are arranged according to increasing wavelength, from left to right a… view at source ↗
Figure 3
Figure 3. Left: example star formation histories drawn from the parameter space investigated during the integrated fits. These star formation histories show different combinations of the multiplicative factor 𝑅 (that controls the overall scaling of the star formation history in the final 100 Myr) and the 𝑒-folding time 𝜏. The inset shows a zoom into the last 200 Myr to better highlight the effect of 𝑅. Right: the resulting sp… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Example radial profiles for different types of galaxies using the classification scheme from Section 3.4. An inside-out quenching galaxy (left) is shown along with an outside-in quenching galaxy (right). Moving from top to bottom, these panels show the stellar mass rad…
Figure 5
Figure 5. Figure 5: Distributions for each morphological metric (columns), separating inside-out quenching galaxies (top, magenta), from outside-in quenching galaxies (bottom, red), after classification (solid lines). Comparison distributions for the corresponding population of simulated …
Figure 6
Figure 6. Figure 6 [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Global star formation rate as a function of stellar mass for the non-quenched+quenched sample of 1939 galaxies. In the background we show unfilled contours (light gray) that denote the location and density of galaxies drawn from COSMOS-Web (Casey et al. 2023) from whic…
Figure 8
Figure 8. Figure 8: Fraction of non-quenched galaxies with SFR > 10−3 𝑀⊙ yr−1 experiencing either inside-out (pink–magenta) or outside-in (orange–red) quenching, as a function of stellar mass. We bin galaxies by environment, separating cluster galaxies (left) from those in the field (righ…
Figure 9
Figure 9. Figure 9: Fraction of non-quenched galaxies with SFR > 10−3 𝑀⊙ yr−1 experiencing either inside-out (pink–dark magenta) or outside-in (orange–red) quenching, as a function of stellar mass. We bin galaxies by phase space-based estimated infall time, 𝑡infall, separating recent infa…
Figure 10
Figure 10. Figure 10: Fraction of non-quenched galaxies, per population and environment, as a function of stellar mass, that reside in a narrower definition of the green valley, similar to Abdurro’uf et al. (2023). We separate the cluster (left) from the field (right). Populations are colo…
Figure 11
Figure 11. Figure 11: Similar to [PITH_FULL_IMAGE:figures/full_fig_p022_11.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

247 extracted references · 1 canonical work pages

  1. [1]

    G., Moore, B., & Bower, R

    Abadi, M. G., Moore, B., & Bower, R. G. 1999, MNRAS, 308, 947 Abdurro’uf, & Akiyama, M. 2018, MNRAS, 479, 5083 Abdurro’uf, Lin, Y.-T., Hirashita, H., et al. 2022, ApJ, 926, 81 Abdurro’uf, Coe, D., Jung, I., et al. 2023, ApJ, 945, 117

    1999

  2. [2]

    Abell, G. O. 1958, ApJS, 3, 211

    1958

  3. [3]

    O., Corwin, Jr., H

    Abell, G. O., Corwin, Jr., H. G., & Olowin, R. P. 1989, ApJS, 70, 1

    1989

  4. [4]

    2019, arXiv:1902.05569

    Akeson, R., Armus, L., Bachelet, E., et al. 2019, arXiv:1902.05569

    arXiv 2019

  5. [5]

    C., Helton, J

    Alberts, S., Williams, C. C., Helton, J. M., et al. 2024, ApJ, 975, 85

    2024

  6. [6]

    1993, A&AS, 97, 851

    Alongi, M., Bertelli, G., Bressan, A., et al. 1993, A&AS, 97, 851

    1993

  7. [7]

    1993, ARA&A, 31, 473

    Antonucci, R. 1993, ARA&A, 31, 473

    1993

  8. [8]

    C., Ilbert, O., Ciesla, L., et al

    Arango-Toro, R. C., Ilbert, O., Ciesla, L., et al. 2025, A&A, 696, A159

    2025

Show all 247 references
  1. [9]

    1999, MNRAS, 310, 540

    Arnouts, S., Cristiani, S., Moscardini, L., et al. 1999, MNRAS, 310, 540

    1999

  2. [10]

    2002, MNRAS, 329, 355 Astropy Collaboration, Robitaille, T

    Arnouts, S., Moscardini, L., Vanzella, E., et al. 2002, MNRAS, 329, 355 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33 Astropy Collaboration, Price-Whelan, A. M., Sip˝ocz, B. M., et al. 2018, AJ, 156, 123 Astropy Collaboration, Price-Whel...

    2002

  3. [11]

    2025, arXiv:2511.07542

    Atek, H., Chisholm, J., Kokorev, V., et al. 2025, arXiv:2511.07542

    2025

  4. [12]

    L., Morris, S

    Balogh, M. L., Morris, S. L., Yee, H. K. C., Carlberg, R. G., & Ellingson, E. 1997, ApJL, 488, L75

    1997

  5. [13]

    L., Morris, S

    Balogh, M. L., Morris, S. L., Yee, H. K. C., Carlberg, R. G., & Ellingson, E. 1999, ApJ, 527, 54

    1999

  6. [14]

    L., Navarro, J

    Balogh, M. L., Navarro, J. F., & Morris, S. L. 2000, ApJ, 540, 113

    2000

  7. [15]

    L., Schade, D., Morris, S

    Balogh, M. L., Schade, D., Morris, S. L., et al. 1998, ApJL, 504, L75

    1998

  8. [16]

    L., McGee, S

    Balogh, M. L., McGee, S. L., Mok, A., et al. 2016, MNRAS, 456, 4364

    2016

  9. [17]

    C., Flynn, K., & Gebhardt, K

    Beers, T. C., Flynn, K., & Gebhardt, K. 1990, AJ, 100, 32

    1990

  10. [18]

    2018, MNRAS, 477, 3014

    Belfiore, F., Maiolino, R., Bundy, K., et al. 2018, MNRAS, 477, 3014

    2018

  11. [19]

    F., Wolf, C., Meisenheimer, K., et al

    Bell, E. F., Wolf, C., Meisenheimer, K., et al. 2004, ApJ, 608, 752

    2004

  12. [20]

    1996, A&AS, 117, 393

    Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393

    1996

  13. [21]

    E., et al

    Bezanson, R., Labbe, I., Whitaker, K. E., et al. 2024, ApJ, 974, 92

    2024

  14. [22]

    2007, MNRAS, 380, 339

    Birnboim, Y., Dekel, A., & Neistein, E. 2007, MNRAS, 380, 339

    2007

  15. [23]

    Bluck, A. F. L., Conselice, C. J., Ormerod, K., et al. 2024, ApJ, 961, 163 B¨ohringer, H., Schuecker, P., Guzzo, L., et al. 2004, A&A, 425, 367

    2024

  16. [24]

    2019, A&A, 622, A103

    Boquien, M., Burgarella, D., Roehlly, Y., et al. 2019, A&A, 622, A103

    2019

  17. [25]

    2022, A&ARv, 30, 3

    Boselli, A., Fossati, M., & Sun, M. 2022, A&ARv, 30, 3

    2022

  18. [26]

    M., Popping, G., et al

    Bottrell, C., Yesuf, H. M., Popping, G., et al. 2024, MNRAS, 527, 6506

    2024

  19. [27]

    G., Benson, A

    Bower, R. G., Benson, A. J., Malbon, R., et al. 2006, MNRAS, 370, 645 Bradaˇc, M., Huang, K.-H., Fontana, A., et al. 2019, MNRAS, 489, 99

    2006

  20. [28]

    2025, astropy/photutils: 2.3.0, 2.3.0, Zenodo, doi: 10.5281/zenodo.17129028

    Bradley, L., Sip˝ocz, B., Robitaille, T., et al. 2025, astropy/photutils: 2.3.0, 2.3.0, Zenodo, doi: 10.5281/zenodo.17129028

    2025 doi

  21. [29]

    B., van Dokkum, P

    Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503

    2008

  22. [30]

    B., van Dokkum, P

    Brammer, G. B., van Dokkum, P. G., Franx, M., et al. 2012, ApJS, 200, 13

    2012

  23. [31]

    N., Phillipps, S., Kelvin, L

    Bremer, M. N., Phillipps, S., Kelvin, L. S., et al. 2018, MNRAS, 476, 12

    2018

  24. [32]

    1993, A&AS, 100, 647

    Bressan, A., Fagotto, F., Bertelli, G., & Chiosi, C. 1993, A&AS, 100, 647

    1993

  25. [33]

    Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151

    2004

  26. [34]

    2003, MNRAS, 344, 1000

    Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000

    2003

  27. [35]

    2013, in Secular Evolution of Galaxies, ed

    Calzetti, D. 2013, in Secular Evolution of Galaxies, ed. J. Falc´on-Barroso & J. H. Knapen, Vol. 419 (Cambridge Univ. Press)

    2013

  28. [36]

    C., et al

    Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682

    2000

  29. [37]

    B., Grillo, C., Rosati, P., et al

    Caminha, G. B., Grillo, C., Rosati, P., et al. 2017, A&A, 600, A90

    2017

  30. [38]

    2003, MNRAS, 342, 345

    Cappellari, M., & Copin, Y. 2003, MNRAS, 342, 345

    2003

  31. [39]

    C., Leja, J., Johnson, B

    Carnall, A. C., Leja, J., Johnson, B. D., et al. 2019, ApJ, 873, 44

    2019

  32. [40]

    C., McLure, R

    Carnall, A. C., McLure, R. J., Dunlop, J. S., & Dav´e, R. 2018, MNRAS, 480, 4379

    2018

  33. [41]

    C., McLeod, D

    Carnall, A. C., McLeod, D. J., McLure, R. J., et al. 2023, MNRAS, 520, 3974

    2023

  34. [42]

    C., Cullen, F., McLure, R

    Carnall, A. C., Cullen, F., McLure, R. J., et al. 2024, MNRAS, 534, 325

    2024

  35. [43]

    M., Kartaltepe, J

    Casey, C. M., Kartaltepe, J. S., Drakos, N. E., et al. 2023, ApJ, 954, 31

    2023

  36. [44]

    2016, A&A, 590, A31

    Castellano, M., Amor´ın, R., Merlin, E., et al. 2016, A&A, 590, A31

    2016

  37. [45]

    2003, PASP, 115, 763

    Chabrier, G. 2003, PASP, 115, 763

    2003

  38. [46]

    2024, MNRAS, 528, 2451

    Chen, H., Xie, L., Wang, J., et al. 2024, MNRAS, 528, 2451

    2024

  39. [47]

    E., Cˆot´e, P., et al

    Cheng, I., Woods, T. E., Cˆot´e, P., et al. 2024, AJ, 167, 178

    2024

  40. [48]

    2016, ApJ, 823, 102

    Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102

    2016

  41. [49]

    2021, A&A, 653, A6

    Ciesla, L., Buat, V., Boquien, M., et al. 2021, A&A, 653, A6

    2021

  42. [50]

    2017, A&A, 608, A41

    Ciesla, L., Elbaz, D., & Fensch, J. 2017, A&A, 608, A41

    2017

  43. [51]

    2018, A&A, 615, A61

    Ciesla, L., Elbaz, D., Schreiber, C., Daddi, E., & Wang, T. 2018, A&A, 615, A61

    2018

  44. [52]

    2016, A&A, 585, A43

    Ciesla, L., Boselli, A., Elbaz, D., et al. 2016, A&A, 585, A43

    2016

  45. [53]

    2023, A&A, 672, A191

    Ciesla, L., G´omez-Guijarro, C., Buat, V., et al. 2023, A&A, 672, A191

    2023

  46. [54]

    2024, A&A, 686, A128

    Ciesla, L., Elbaz, D., Ilbert, O., et al. 2024, A&A, 686, A128

    2024

  47. [55]

    2019, ApJ, 884, 85

    Coe, D., Salmon, B., Bradaˇc, M., et al. 2019, ApJ, 884, 85

    2019

  48. [56]

    G., Baugh, C

    Cole, S., Lacey, C. G., Baugh, C. M., & Frenk, C. S. 2000, MNRAS, 319, 168

    2000

  49. [57]

    A., et al

    Collette, A., Kluyver, T., Caswell, T. A., et al. 2023, h5py/h5py: 3.8.0, v3.8.0, Zenodo, doi: 10.5281/zenodo.7560547

    2023 doi

  50. [58]

    Conroy, C., & Gunn, J. E. 2010, ApJ, 712, 833 Cˆot´e, P., Woods, T. E., Hutchings, J. B., et al. 2025, JATIS, 11, 042202

    2010

  51. [59]

    J., & Newell, E

    Couch, W. J., & Newell, E. B. 1984, ApJS, 56, 143

    1984

  52. [60]

    1967, ITIT, 13, 21

    Cover, T., & Hart, P. 1967, ITIT, 13, 21

    1967

  53. [61]

    A., & van de Voort, F

    Crain, R. A., & van de Voort, F. 2023, ARA&A, 61, 473

    2023

  54. [62]

    J., Springel, V., White, S

    Croton, D. J., Springel, V., White, S. D. M., et al. 2006, MNRAS, 365, 11

    2006

  55. [63]

    A., Helou, G., Magdis, G

    Dale, D. A., Helou, G., Magdis, G. E., et al. 2014, ApJ, 784, 83

    2014

  56. [64]

    J., Crain, R

    Davies, J. J., Crain, R. A., Oppenheimer, B. D., & Schaye, J. 2020, MNRAS, 491, 4462

    2020

  57. [65]

    Davies, L. J. M., Robotham, A. S. G., Lagos, C. d. P., et al. 2019, MNRAS, 483, 5444 De Lucia, G., & Blaizot, J. 2007, MNRAS, 375, 2 Di Criscienzo, M., Merlin, E., Castellano, M., et al. 2017, A&A, 607, A30 Di Matteo, T., Springel, V., & Hernquist, L. 2005, Natur, 433, 604

    2019

  58. [66]

    1999, MNRAS, 309, 610

    Diaferio, A. 1999, MNRAS, 309, 610

    1999

  59. [67]

    Diaferio, A., & Geller, M. J. 1997, ApJ, 481, 633

    1997

  60. [68]

    Diemer, B., Stevens, A. R. H., Lagos, C. d. P., et al. 2019, MNRAS, 487, 1529

    2019

  61. [69]

    2019, MNRAS, 485, 4817

    Donnari, M., Pillepich, A., Nelson, D., et al. 2019, MNRAS, 485, 4817

    2019

  62. [70]

    D., et al

    Donnari, M., Pillepich, A., Joshi, G. D., et al. 2021b, MNRAS, 500, 4004 Dor´e, O., Hirata, C., Wang, Y., et al. 2018, arXiv:1804.03628

    2018 arXiv

  63. [71]

    2025, A&A, 699, A95

    Dou, H., & Yu, H. 2025, A&A, 699, A95

    2025

  64. [72]

    M., et al

    Dressler, A., Smail, I., Poggianti, B. M., et al. 1999, ApJS, 122, 51

    1999

  65. [73]

    2007, ApJL, 661, L33

    Ebeling, H., Barrett, E., Donovan, D., et al. 2007, ApJL, 661, L33

    2007

  66. [74]

    C., & Henry, J

    Ebeling, H., Edge, A. C., & Henry, J. P. 2001, ApJ, 553, 668

    2001

  67. [75]

    2014, ApJS, 211, 21

    Ebeling, H., Ma, C.-J., & Barrett, E. 2014, ApJS, 211, 21

    2014

  68. [76]

    C., Ebeling, H., Bremer, M., et al

    Edge, A. C., Ebeling, H., Bremer, M., et al. 2003, MNRAS, 339, 913

    2003

  69. [77]

    J., Willott, C., Alberts, S., et al

    Eisenstein, D. J., Willott, C., Alberts, S., et al. 2026, ApJS, 283, 6

    2026

  70. [78]

    2024, OJAp, 7, 121

    Ellison, S., Ferreira, L., Wild, V., et al. 2024, OJAp, 7, 121

    2024

  71. [79]

    L., Wilkinson, S., Woo, J., et al

    Ellison, S. L., Wilkinson, S., Woo, J., et al. 2022, MNRAS, 517, L92 Euclid Collaboration, Scaramella, R., Amiaux, J., et al. 2022, A&A, 662, A112 Euclid Collaboration, Mellier, Y., Abdurro’uf, et al. 2025, A&A, 697, A1

    2022

  72. [80]

    M., Willmer, C

    Faber, S. M., Willmer, C. N. A., Wolf, C., et al. 2007, ApJ, 665, 265

    2007

  73. [81]

    Fabian, A. C. 2012, ARA&A, 50, 455

    2012

  74. [82]

    2026, in Encyclopedia of Astrophysics, Volume 4, Vol

    Feldmann, R., & Bieri, R. 2026, in Encyclopedia of Astrophysics, Volume 4, Vol. 4, 576–599, doi: 10.1016/B978-0-443-21439-4.00111-5

    2026 doi

  75. [83]

    J., Chatzikos, M., Guzm´an, F., et al

    Ferland, G. J., Chatzikos, M., Guzm´an, F., et al. 2017, RMxAA, 53, 385

    2017

  76. [84]

    Fix, E., & Hodges, J. L. 1951, Discriminatory Analysis—Nonparametric Discrimination: Consistency Properties, Tech. Rep. 4: 21-49-004, USAF School of Aviation Medicine, Randolph Field, TX

    1951

  77. [85]

    Fix, E., & Hodges, J. L. 1989, International Statistical Review, 57, 238

    1989

  78. [86]

    2011, A&A, 528, A46

    Fumagalli, M., Gavazzi, G., Scaramella, R., & Franzetti, P. 2011, A&A, 528, A46

    2011

  79. [87]

    P., Mather, J

    Gardner, J. P., Mather, J. C., Clampin, M., et al. 2006, SSRv, 123, 485

    2006

  80. [88]

    P., Mather, J

    Gardner, J. P., Mather, J. C., Abbott, R., et al. 2023, PASP, 135, 068001

    2023

  81. [89]

    2013, A&A, 553, A89

    Gavazzi, G., Fumagalli, M., Fossati, M., et al. 2013, A&A, 553, A89

    2013

  82. [90]

    Gensior, J., Kruijssen, J. M. D., & Keller, B. W. 2020, MNRAS, 495, 199

    2020

  83. [91]

    1996, A&AS, 117, 113

    Girardi, L., Bressan, A., Chiosi, C., Bertelli, G., & Nasi, E. 1996, A&AS, 117, 113

    1996

  84. [92]

    2017, MNRAS, 466, 4731 G´omez, P

    Goddard, D., Thomas, D., Maraston, C., et al. 2017, MNRAS, 466, 4731 G´omez, P. L., Nichol, R. C., Miller, C. J., et al. 2003, ApJ, 584, 210 G´omez, P. L., Valkonen, L. E., Romer, A. K., et al. 2012, AJ, 144, 79 23 The Open Journal of Astrophysics Lawlor-Forsyth et al. Gonz´al...

    2017

  85. [93]

    A., Kocevski, D

    Grogin, N. A., Kocevski, D. D., Faber, S. M., et al. 2011, ApJS, 197, 35

    2011

  86. [94]

    E., & Gott, III, J

    Gunn, J. E., & Gott, III, J. R. 1972, ApJ, 176, 1

    1972

  87. [95]

    2019, ApJ, 870, 19

    Guo, K., Peng, Y., Shao, L., et al. 2019, ApJ, 870, 19

    2019

  88. [96]

    S., Li, Y., et al

    Habouzit, M., Somerville, R. S., Li, Y., et al. 2022, MNRAS, 509, 3015

    2022

  89. [97]

    R., Millman, K

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

    2020

  90. [98]

    Hester, J. A. 2006, ApJ, 647, 910

    2006

  91. [99]

    L., et al

    Hewitt, G., Sarron, F., Balogh, M. L., et al. 2025, MNRAS, 541, 409

    2025

  92. [100]

    Hodges, J. L. 1958, ArM, 3, 469

    1958

  93. [101]

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

    2007

  94. [102]

    J., et al

    Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, A&A, 457, 841

    2006

  95. [103]

    2014, MNRAS, 443, 1549

    Jauzac, M., Cl´ement, B., Limousin, M., et al. 2014, MNRAS, 443, 1549

    2014

  96. [104]

    D., Leja, J., Conroy, C., & Speagle, J

    Johnson, B. D., Leja, J., Conroy, C., & Speagle, J. S. 2021, ApJS, 254, 22

    2021

  97. [105]

    I., Caminha, G

    Karman, W., Caputi, K. I., Caminha, G. B., et al. 2017, A&A, 599, A28

    2017

  98. [106]

    Kauffmann, G., White, S. D. M., Heckman, T. M., et al. 2004, MNRAS, 353, 713

    2004

  99. [107]

    Kawata, D., & Mulchaey, J. S. 2008, ApJL, 672, L103

    2008

  100. [108]

    C., & Evans, N

    Kennicutt, R. C., & Evans, N. J. 2012, ARA&A, 50, 531

    2012

  101. [109]

    L., Smail, I., Bower, R

    Kodama, T., Balogh, M. L., Smail, I., Bower, R. G., & Nakata, F. 2004, MNRAS, 354, 1103

    2004

  102. [110]

    M., Faber, S

    Koekemoer, A. M., Faber, S. M., Ferguson, H. C., et al. 2011, ApJS, 197, 36

    2011

  103. [111]

    2011, ApJ, 734, 66

    Koyama, Y., Kodama, T., Nakata, F., Shimasaku, K., & Okamura, S. 2011, ApJ, 734, 66

    2011

  104. [112]

    2010, MNRAS, 403, 1611

    Koyama, Y., Kodama, T., Shimasaku, K., et al. 2010, MNRAS, 403, 1611

    2010

  105. [113]

    2018, PASJ, 70, S21

    Koyama, Y., Hayashi, M., Tanaka, M., et al. 2018, PASJ, 70, S21

    2018

  106. [114]

    G., Labb´e, I., et al

    Kriek, M., van Dokkum, P. G., Labb´e, I., et al. 2009, ApJ, 700, 221

    2009

  107. [115]

    Kristian, J., Sandage, A., & Westphal, J. A. 1978, ApJ, 221, 383

    1978

  108. [116]

    R., Harrison, F

    Kulkarni, S. R., Harrison, F. A., Grefenstette, B. W., et al. 2021, arXiv:2111.15608

    2021 arXiv

  109. [117]

    J., Richard, J., Cl´ement, B., et al

    Lagattuta, D. J., Richard, J., Cl´ement, B., et al. 2017, MNRAS, 469, 3946

    2017

  110. [118]

    J., Richard, J., Bauer, F

    Lagattuta, D. J., Richard, J., Bauer, F. E., et al. 2019, MNRAS, 485, 3738

    2019

  111. [119]

    B., Tinsley, B

    Larson, R. B., Tinsley, B. M., & Caldwell, C. N. 1980, ApJ, 237, 692

    1980

  112. [120]

    R., Postman, M., Strauss, M

    Lauer, T. R., Postman, M., Strauss, M. A., Graves, G. J., & Chisari, N. E. 2014, ApJ, 797, 82

    2014

  113. [121]

    2026, arXiv:2607.20406

    Lawlor-Forsyth, C. 2026, arXiv:2607.20406

    2026 arXiv

  114. [122]

    L., McGee, S

    Lawlor-Forsyth, C., Balogh, M. L., McGee, S. L., & Rudnick, G. H. 2026a, arXiv:2607.15638

    arXiv

  115. [123]

    C., Johnson, B

    Leja, J., Carnall, A. C., Johnson, B. D., Conroy, C., & Speagle, J. S. 2019, ApJ, 876, 3

    2019

  116. [124]

    D., Conroy, C., van Dokkum, P

    Leja, J., Johnson, B. D., Conroy, C., van Dokkum, P. G., & Byler, N. 2017, ApJ, 837, 170

    2017

  117. [125]

    2002, MNRAS, 334, 673

    Lewis, I., Balogh, M., De Propris, R., et al. 2002, MNRAS, 334, 673

    2002

  118. [126]

    2019, ApJ, 872, 50

    Lin, L., Hsieh, B.-C., Pan, H.-A., et al. 2019, ApJ, 872, 50

    2019

  119. [127]

    2011, MNRAS, 410, 166

    Lintott, C., Schawinski, K., Bamford, S., et al. 2011, MNRAS, 410, 166

    2011

  120. [128]

    J., Schawinski, K., Slosar, A., et al

    Lintott, C. J., Schawinski, K., Slosar, A., et al. 2008, MNRAS, 389, 1179

    2008

  121. [129]

    M., Koekemoer, A., Coe, D., et al

    Lotz, J. M., Koekemoer, A., Coe, D., et al. 2017, ApJ, 837, 97 LSST Science Collaboration, Abell, P. A., Allison, J., et al. 2009, arXiv:0912.0201

    2017 arXiv

  122. [130]

    2022, ApJ, 941, 205

    Ma, W., Liu, K., Guo, H., et al. 2022, ApJ, 941, 205

    2022

  123. [131]

    2014, ARA&A, 52, 415

    Madau, P., & Dickinson, M. 2014, ARA&A, 52, 415

    2014

  124. [132]

    A., & Raychaudhury, S

    Mahajan, S., Mamon, G. A., & Raychaudhury, S. 2011, MNRAS, 416, 2882

    2011

  125. [133]

    2018, MNRAS, 473, 663

    Mahler, G., Richard, J., Cl´ement, B., et al. 2018, MNRAS, 473, 663

    2018

  126. [134]

    W., & Ebeling, H

    Mann, A. W., & Ebeling, H. 2012, MNRAS, 420, 2120

    2012

  127. [135]

    2025, in Wide Field Camera 3 Instrument

    Marinelli, M., Green, J., et al. 2025, in Wide Field Camera 3 Instrument

    2025

  128. [136]

    A., Amen, L., Woods, T

    Marshall, M. A., Amen, L., Woods, T. E., et al. 2025, MNRAS, 537, 1703

    2025

  129. [137]

    2009, ApJ, 707, 250

    Martig, M., Bournaud, F., Teyssier, R., & Dekel, A. 2009, ApJ, 707, 250

    2009

  130. [138]

    C., Fanson, J., Schiminovich, D., et al

    Martin, D. C., Fanson, J., Schiminovich, D., et al. 2005, ApJL, 619, L1

    2005

  131. [139]

    C., Wyder, T

    Martin, D. C., Wyder, T. K., Schiminovich, D., et al. 2007, ApJS, 173, 342

    2007

  132. [140]

    B., et al

    Matharu, J., Muzzin, A., Brammer, G. B., et al. 2021, ApJ, 923, 222

    2021

  133. [141]

    C., et al

    Matharu, J., Papovich, C., Simons, R. C., et al. 2022, ApJ, 937, 16

    2022

  134. [142]

    McDonough, B., Curtis, O., & Brainerd, T. G. 2023, ApJ, 958, 19

    2023

  135. [143]

    McDonough, B., Curtis, O., & Brainerd, T. G. 2025, ApJ, 978, 67

    2025

  136. [144]

    L., van der Burg, R

    McNab, K., Balogh, M. L., van der Burg, R. F. J., et al. 2021, MNRAS, 508, 157

    2021

  137. [145]

    R., & Nulsen, P

    McNamara, B. R., & Nulsen, P. E. J. 2007, ARA&A, 45, 117

    2007

  138. [146]

    R., & Nulsen, P

    McNamara, B. R., & Nulsen, P. E. J. 2012, New Journal of Physics, 14, 055023

    2012

  139. [147]

    2016, ApJ, 817, 24

    Medezinski, E., Umetsu, K., Okabe, N., et al. 2016, ApJ, 817, 24

    2016

  140. [148]

    2016, A&A, 590, A30

    Merlin, E., Amor´ın, R., Castellano, M., et al. 2016, A&A, 590, A30

    2016

  141. [149]

    D., Schaye, J., Bower, R

    Mitchell, P. D., Schaye, J., Bower, R. G., & Crain, R. A. 2020, MNRAS, 494, 3971

    2020

  142. [150]

    G., Brammer, G

    Momcheva, I. G., Brammer, G. B., van Dokkum, P. G., et al. 2016, ApJS, 225, 27

    2016

  143. [151]

    1996, Natur, 379, 613

    Moore, B., Katz, N., Lake, G., Dressler, A., & Oemler, A. 1996, Natur, 379, 613

    1996

  144. [152]

    1998, ApJ, 495, 139

    Moore, B., Lake, G., & Katz, N. 1998, ApJ, 495, 139

    1998

  145. [153]

    1999, MNRAS, 304, 465

    Moore, B., Lake, G., Quinn, T., & Stadel, J. 1999, MNRAS, 304, 465

    1999

  146. [154]

    E., Treu, T., et al

    Morishita, T., Abramson, L. E., Treu, T., et al. 2017, ApJ, 835, 254

    2017

  147. [155]

    2019, A&A, 626, A61

    Morselli, L., Popesso, P., Cibinel, A., et al. 2019, A&A, 626, A61

    2019

  148. [156]

    L., Aird, J., et al

    Moustakas, J., Coil, A. L., Aird, J., et al. 2013, ApJ, 767, 50

    2013

  149. [157]

    Muzzin, A., Wilson, G., Yee, H. K. C., et al. 2012, ApJ, 746, 188

    2012

  150. [158]

    2024, NatSR, 14, 3724

    Nanayakkara, T., Glazebrook, K., Jacobs, C., et al. 2024, NatSR, 14, 3724

    2024

  151. [159]

    B., Muzzin, A., van der Burg, R

    Nantais, J. B., Muzzin, A., van der Burg, R. F. J., et al. 2017, MNRAS, 465, L104

    2017

  152. [160]

    V., H¨außler, B., Marchesini, D., et al

    Nedkova, K. V., H¨außler, B., Marchesini, D., et al. 2021, MNRAS, 506, 928

    2021

  153. [161]

    2024, A&A, 686, A157

    Nelson, D., Pillepich, A., Ayromlou, M., et al. 2024, A&A, 686, A157

    2024

  154. [162]

    2018, MNRAS, 475, 624

    Nelson, D., Pillepich, A., Springel, V., et al. 2018, MNRAS, 475, 624

    2018

  155. [163]

    J., Tacchella, S., Diemer, B., et al

    Nelson, E. J., Tacchella, S., Diemer, B., et al. 2021, MNRAS, 508, 219

    2021

  156. [164]

    2023, MNRAS, 524, 2883

    Niemiec, A., Jauzac, M., Eckert, D., et al. 2023, MNRAS, 524, 2883

    2023

  157. [165]

    2026, ApJ, 1001, 82

    Olsen, C., Gawiser, E., Welker, C., et al. 2026, ApJ, 1001, 82

    2026

  158. [166]

    A., & Hudson, M

    Oman, K. A., & Hudson, M. J. 2016, MNRAS, 463, 3083

    2016

  159. [167]

    A., Hudson, M

    Oman, K. A., Hudson, M. J., & Behroozi, P. S. 2013, MNRAS, 431, 2307

    2013

  160. [168]

    S., Randall, S

    Owers, M. S., Randall, S. W., Nulsen, P. E. J., et al. 2011, ApJ, 728, 27

    2011

  161. [169]

    J., Davidzon, I., et al

    Pagul, A., S´anchez, F. J., Davidzon, I., et al. 2024, ApJS, 273, 10

    2024

  162. [170]

    2019, MNRAS, 484, 1702

    Pasquali, A., Smith, R., Gallazzi, A., et al. 2019, MNRAS, 484, 1702

    2019

  163. [171]

    2011, JMLR, 12, 2825

    Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, JMLR, 12, 2825

    2011

  164. [172]

    J., Renzini, A., & Carollo, M

    Peng, Y., Lilly, S. J., Renzini, A., & Carollo, M. 2012, ApJ, 757, 4

    2012

  165. [173]

    J., Renzini, A., & Carollo, M

    Peng, Y., Lilly, S. J., Renzini, A., & Carollo, M. 2014, ApJ, 790, 95

    2014

  166. [174]

    2015, Natur, 521, 192

    Peng, Y., Maiolino, R., & Cochrane, R. 2015, Natur, 521, 192

    2015

  167. [175]

    J., Kovaˇc, K., et al

    Peng, Y., Lilly, S. J., Kovaˇc, K., et al. 2010, ApJ, 721, 193

    2010

  168. [176]

    N., Hopkins, A

    Phillipps, S., Bremer, M. N., Hopkins, A. M., et al. 2019, MNRAS, 485, 5559

    2019

  169. [177]

    2019, MNRAS, 490, 3196 Planck Collaboration, Ade, P

    Pillepich, A., Nelson, D., Springel, V., et al. 2019, MNRAS, 490, 3196 Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2016, A&A, 594, A13

    2019

  170. [178]

    M., Moretti, A., Gullieuszik, M., et al

    Poggianti, B. M., Moretti, A., Gullieuszik, M., et al. 2017, ApJ, 844, 48

    2017

  171. [179]

    M., Vulcani, B., Tomicic, N., et al

    Poggianti, B. M., Vulcani, B., Tomicic, N., et al. 2025, A&A, 699, A357

    2025

  172. [180]

    2012, ApJS, 199, 25

    Postman, M., Coe, D., Ben´ıtez, N., et al. 2012, ApJS, 199, 25

    2012

  173. [181]

    H., Ellison, S

    Quai, S., Hani, M. H., Ellison, S. L., Patton, D. R., & Woo, J. 2021, MNRAS, 504, 1888

    2021

  174. [182]

    2000, Sci, 288, 1617

    Quilis, V., Moore, B., & Bower, R. 2000, Sci, 288, 1617

    2000

  175. [183]

    Regos, E., & Geller, M. J. 1989, AJ, 98, 755

    1989

  176. [184]

    2017, ApJ, 843, 128

    Rhee, J., Smith, R., Choi, H., et al. 2017, ApJ, 843, 128

    2017

  177. [185]

    2023, PASP, 135, 048001

    Rigby, J., Perrin, M., McElwain, M., et al. 2023, PASP, 135, 048001

    2023

  178. [186]

    D., & Parker, L

    Roberts, I. D., & Parker, L. C. 2020, MNRAS, 495, 554

    2020

  179. [187]

    2005, A&A, 433, 875 Roman Observations Time Allocation Committee, & Core Community Survey Definition Committees

    Roediger, E., & Hensler, G. 2005, A&A, 433, 875 Roman Observations Time Allocation Committee, & Core Community Survey Definition Committees. 2025, arXiv:2505.10574

    2005

  180. [188]

    2018, MNRAS, 480, 2544 S´anchez, S

    Rowlands, K., Heckman, T., Wild, V., et al. 2018, MNRAS, 480, 2544 S´anchez, S. F., Avila-Reese, V., Hernandez-Toledo, H., et al. 2018, RMxAA, 54, 217 S´anchez-Bl´azquez, P., Peletier, R. F., Jim´enez-Vicente, J., et al. 2006, MNRAS, 371, 703

    2018

  181. [189]

    Sarrouh, G. T. E., Asada, Y., Martis, N. S., et al. 2026, ApJS, 282, 3

    2026

  182. [190]

    L., Croom, S

    Schaefer, A. L., Croom, S. M., Allen, J. T., et al. 2017, MNRAS, 464, 121

    2017

  183. [191]

    M., Simmons, B

    Schawinski, K., Urry, C. M., Simmons, B. D., et al. 2014, MNRAS, 440, 889

    2014

  184. [192]

    2007, ApJS, 172, 1

    Scoville, N., Aussel, H., Brusa, M., et al. 2007, ApJS, 172, 1

    2007

  185. [193]

    Seyfert, C. K. 1943, ApJ, 97, 28

    1943

  186. [194]

    V., Lange-Vagle, D., Marchesini, D., et al

    Shipley, H. V., Lange-Vagle, D., Marchesini, D., et al. 2018, ApJS, 235, 14

    2018

  187. [195]

    B., Paquereau, L., et al

    Shuntov, M., Akins, H. B., Paquereau, L., et al. 2025, A&A, 704, A339

    2025

  188. [196]

    E., Whitaker, K

    Skelton, R. E., Whitaker, K. E., Momcheva, I. G., et al. 2014, ApJS, 214, 24

    2014

  189. [197]

    J., Lintott, C

    Smethurst, R. J., Lintott, C. J., Simmons, B. D., et al. 2015, MNRAS, 450, 435

    2015

  190. [198]

    N., et al

    Sobral, D., Smail, I., Best, P. N., et al. 2013, MNRAS, 428, 1128 24 The Open Journal of Astrophysics Lawlor-Forsyth et al

    2013

  191. [199]

    S., Steinhardt, C

    Speagle, J. S., Steinhardt, C. L., Capak, P. L., & Silverman, J. D. 2014, ApJS, 214, 15

    2014

  192. [200]

    2013, arXiv:1305.5422

    Spergel, D., Gehrels, N., Breckinridge, J., et al. 2013, arXiv:1305.5422

    2013 arXiv

  193. [201]

    2015, arXiv:1503.03757

    Spergel, D., Gehrels, N., Baltay, C., et al. 2015, arXiv:1503.03757

    2015 arXiv

  194. [202]

    2018, MNRAS, 475, 676

    Springel, V., Pakmor, R., Pillepich, A., et al. 2018, MNRAS, 475, 676

    2018

  195. [203]

    V., et al

    Stark, D. V., et al. 2025, in Advanced Camera for Surveys Instrument

    2025

  196. [204]

    K., Tonnesen, S., & Kopenhafer, C

    Starkenburg, T. K., Tonnesen, S., & Kopenhafer, C. 2019, ApJL, 874, L17

    2019

  197. [205]

    L., Jauzac, M., Acebron, A., et al

    Steinhardt, C. L., Jauzac, M., Acebron, A., et al. 2020, ApJS, 247, 64

    2020

  198. [206]

    Stevens, A. R. H., Diemer, B., Lagos, C. d. P., et al. 2019, MNRAS, 483, 5334

    2019

  199. [207]

    Stevens, A. R. H., Lagos, C. d. P., Cortese, L., et al. 2021, MNRAS, 502, 3158

    2021

  200. [208]

    Stevens, A. R. H., Brown, T., Diemer, B., et al. 2023, ApJL, 957, L19

    2023

  201. [209]

    F., & Rood, H

    Struble, M. F., & Rood, H. J. 1991, ApJS, 77, 363

    1991

  202. [210]

    F., Hayward, C

    Su, K.-Y., Hopkins, P. F., Hayward, C. C., et al. 2019, MNRAS, 487, 4393

    2019

  203. [211]

    A., Weaver, J

    Suess, K. A., Weaver, J. R., Price, S. H., et al. 2024, ApJ, 976, 101

    2024

  204. [212]

    M., et al

    Tacchella, S., Dekel, A., Carollo, C. M., et al. 2016, MNRAS, 458, 242

    2016

  205. [213]

    M., Renzini, A., et al

    Tacchella, S., Carollo, C. M., Renzini, A., et al. 2015, Sci, 348, 314

    2015

  206. [214]

    M., F¨orster Schreiber, N

    Tacchella, S., Carollo, C. M., F¨orster Schreiber, N. M., et al. 2018, ApJ, 859, 56

    2018

  207. [215]

    Tan, V. Y. Y., Muzzin, A., Marsan, Z. C., et al. 2022, ApJ, 933, 30

    2022

  208. [216]

    Taylor, M. B. 2005, ASPC, 347, 29

    2005

  209. [217]

    A., Bell, E

    Terrazas, B. A., Bell, E. F., Pillepich, A., et al. 2020, MNRAS, 493, 1888

    2020

  210. [218]

    B., Brammer, G

    Treu, T., Schmidt, K. B., Brammer, G. B., et al. 2015, ApJ, 812, 114

    2015

  211. [219]

    2022, ApJ, 935, 110

    Treu, T., Roberts-Borsani, G., Bradac, M., et al. 2022, ApJ, 935, 110

    2022

  212. [220]

    S., & Werk, J

    Tumlinson, J., Peeples, M. S., & Werk, J. K. 2017, ARA&A, 55, 389

    2017

  213. [221]

    2011, ApJ, 729, 127

    Umetsu, K., Broadhurst, T., Zitrin, A., Medezinski, E., & Hsu, L.-Y. 2011, ApJ, 729, 127

    2011

  214. [222]

    2016, ApJ, 821, 116

    Umetsu, K., Zitrin, A., Gruen, D., et al. 2016, ApJ, 821, 116

    2016

  215. [223]

    M., & Padovani, P

    Urry, C. M., & Padovani, P. 1995, PASP, 107, 803 van der Burg, R. F. J., Rudnick, G., Balogh, M. L., et al. 2020, A&A, 638, A112

    1995

  216. [224]

    Vink, J. S. 2020, Galax, 8, 43

    2020

  217. [225]

    E., et al

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

    2020

  218. [226]

    2020, NatRP, 2, 42

    Vogelsberger, M., Marinacci, F., Torrey, P., & Puchwein, E. 2020, NatRP, 2, 42

    2020

  219. [227]

    M., Oppenheimer, B

    Voit, G. M., Oppenheimer, B. D., Bell, E. F., Terrazas, B., & Donahue, M. 2024, ApJ, 960, 28 Von Der Linden, A., Best, P. N., Kauffmann, G., & White, S. D. M. 2007, MNRAS, 379, 867

    2024

  220. [228]

    B., et al

    Vulcani, B., Treu, T., Schmidt, K. B., et al. 2015, ApJ, 814, 161

    2015

  221. [229]

    B., et al

    Vulcani, B., Treu, T., Schmidt, K. B., et al. 2016, ApJ, 833, 178

    2016

  222. [230]

    2017, ApJ, 837, 126

    Vulcani, B., Treu, T., Nipoti, C., et al. 2017, ApJ, 837, 126

    2017

  223. [231]

    2024, ApJS, 270, 12

    Wang, B., Leja, J., Labb´e, I., et al. 2024, ApJS, 270, 12

    2024

  224. [232]

    I., Smith, B

    Wang, X., Teplitz, H. I., Smith, B. M., et al. 2025, ApJ, 980, 74

    2025

  225. [233]

    R., Cutler, S

    Weaver, J. R., Cutler, S. E., Pan, R., et al. 2024, ApJS, 270, 7

    2024

  226. [234]

    K., Schawinski, K., Caplar, N., et al

    Weigel, A. K., Schawinski, K., Caplar, N., et al. 2017, ApJ, 845, 145

    2017

  227. [235]

    2017, MNRAS, 465, 3291

    Weinberger, R., Springel, V., Hernquist, L., et al. 2017, MNRAS, 465, 3291

    2017

  228. [236]

    R., Tinker, J

    Wetzel, A. R., Tinker, J. L., & Conroy, C. 2012, MNRAS, 424, 232

    2012

  229. [237]

    R., Tinker, J

    Wetzel, A. R., Tinker, J. L., Conroy, C., & van den Bosch, F. C. 2013, MNRAS, 432, 336

    2013

  230. [238]

    E., van Dokkum, P

    Whitaker, K. E., van Dokkum, P. G., Brammer, G., & Franx, M. 2012, ApJL, 754, L29

    2012

  231. [239]

    E., Franx, M., Leja, J., et al

    Whitaker, K. E., Franx, M., Leja, J., et al. 2014, ApJ, 795, 104

    2014

  232. [240]

    C., Tacchella, S., Maseda, M

    Williams, C. C., Tacchella, S., Maseda, M. V., et al. 2023, ApJS, 268, 64

    2023

  233. [241]

    J., Doyon, R., Albert, L., et al

    Willott, C. J., Doyon, R., Albert, L., et al. 2022, PASP, 134, 025002

    2022

  234. [242]

    A., Cohen, S

    Windhorst, R. A., Cohen, S. H., Jansen, R. A., et al. 2023, AJ, 165, 13

    2023

  235. [243]

    S., Rieke, G

    Wright, G. S., Rieke, G. H., Glasse, A., et al. 2023, PASP, 135, 048003

    2023

  236. [244]

    J., Lagos, C

    Wright, R. J., Lagos, C. d. P., Davies, L. J. M., et al. 2019, MNRAS, 487, 3740

    2019

  237. [245]

    J., Somerville, R

    Wright, R. J., Somerville, R. S., Lagos, C. d. P., et al. 2024, MNRAS, 532, 3417

    2024

  238. [246]

    G., Adelman, J., Anderson, Jr., J

    York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579

    2000

  239. [247]

    C., et al

    Zhang, C., Peng, Y., Ho, L. C., et al. 2021, ApJ, 911, 57 This paper was built using The Open Journal of Astrophysics LATEX template. The Open Journal of Astrophysics is a journal which provides fast and easy peer review for new papers in theastro-phsection of the arXiv, makin...

    2021

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.