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Morphology across cosmic time: assessing the evolution and interplay of disk and bulge-dominated galaxies in the CANDELS survey

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

Pith's one-line read Bulge-dominated galaxies split into two evolutionary tracks below $z \sim 1.6$: G1, star-forming and disk-like at all redshifts, and G2, massive and quenched, built by merger-driven transformation of massive disks.

desk verdict A credible but conditional claim of two bulge evolutionary tracks; the missing classifier validation and plausible disk contamination keep me from endorsing it as is. read the letter →

arxiv 2506.12205 v2 pith:7PFPVQZA submitted 2025-06-13 astro-ph.GA

classification astro-ph.GA
keywords galaxyevolutionmorphologystarformationhigh-redshiftgalaxiesbulge-dominatedCANDELSsurveyGaussianmixturedecompositionSérsicindex
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

Using a mass-complete sample of about 14,000 galaxies from the CANDELS survey spanning $0.2 \le z \le 2.4$, the paper claims that disk and bulge-dominated galaxies begin with nearly identical specific star formation rates at $z \sim 2.4$ but diverge sharply below $z < 1.6$. The divergence takes a specific form: the sSFR distribution of bulge-dominated galaxies becomes bimodal, splitting into G1, a long-lived star-forming population with disk-like properties, and G2, a quenched population that is about 0.6 dex more massive, about 0.9 dex less active in star formation, and increasingly dominant toward the present. The paper argues the split is driven by stellar mass assembly through major mergers of massive disks, not by smooth quenching alone, and that the transformation is strongly mass-dependent. If correct, bulge-dominated galaxies are not one population with one formation channel but two families following divergent physical pathways.

What carries the argument

The argument rides on two tools. The first is an eye-free, redshift-binned morphological classification built from the MEGG non-parametric indices — second moment of light $M_{20}$, Shannon entropy, Gini coefficient, and gradient field asymmetry — grouped by a self-organizing map and labeled by an ensemble of convolutional neural networks, so that galaxies count as disk- or bulge-dominated without visual inspection or parametric light-profile assumptions. The second is a Gaussian mixture decomposition (Bayesian and frequentist, with the component number chosen by AIC/BIC) applied to the sSFR distribution of bulge-dominated galaxies in each redshift bin, which isolates the two tracks G1 and G2. Sérsic structural parameters (effective radius $R_e$, Sérsic index $n_s$) from multi-band profile fits and SED-based stellar masses and star formation rates from the CANDELS catalogs supply the physical properties that give the two components distinct identities.

What would settle it

Re-derive the disk/bulge labels for the same galaxies with an independent method — spatially resolved stellar kinematics from integral-field spectroscopy at $0.5 < z < 1.5$, or a classifier built on different morphological indices — and re-run the Gaussian mixture decomposition on the bulge-dominated subset. If the two-component sSFR split and the massive, high-Sérsic G2 population disappear under the alternative labels, the classification was creating the signal; if they persist, the two-track picture holds.

Watch

Extended reading notes

Core claim

The central claim is that bulge-dominated galaxies are heterogeneous: at $z < 1.6$ their sSFR distribution separates into two Gaussian components with a consistent mean offset of $\langle\Delta\mu\rangle = 1.31 \pm 0.19$ dex. G1 systems remain on the blue cloud of the star-forming main sequence at every redshift, with Sérsic indices and effective radii close to disk values, indicating sustained star formation despite a bulge-dominated morphology. G2 systems are more massive ($\Delta\log M_*/M_\odot = 0.61 \pm 0.16$ dex), have lower star formation rates ($\Delta\log\mathrm{SFR} = 0.92 \pm 0.26$ dex), higher Sérsic indices ($\Delta n_s = 0.99 \pm 0.35$) at similar effective radii, and migrate from the blue cloud into the green valley and red sequence as redshift declines; their mean stellar mass changes little, arguing against growth through in-situ star formation. The paper reads this as merger-driven transformation of massive disks into quenched, centrally concentrated remnants: between $z = 2.4$ and $z = 0.2$ the fraction of massive ($\log M_*/M_\odot > 10.5$) disks falls from 55% to 5% while the bulge-dominated fraction rises from 25% to 90%, with the steepest trends in exactly the mass bin where G2 grows.

Load-bearing premise

The redshift-binned morphological classifier adopted from the companion catalog must be accurate enough that the disk/bulge labels do not themselves manufacture the observed bimodality; the paper reports that up to 18% of disks could be misclassified as bulge-dominated without correction, and the corrected classifier is taken as input rather than independently validated here.

Editorial extensions

If this is right

  • Below $z < 1.6$, bulge-dominated galaxies are bimodal in both sSFR and stellar mass, so analyses that treat them as a single population average over two distinct evolutionary states.
  • The most massive disks ($\log M_*/M_\odot > 10.5$) drop from 55% to 5% of the morphological mix between $z = 2.4$ and $z = 0.2$ while massive bulge-dominated systems rise from 25% to 90%, implying a mass-dependent transformation that reshapes the high-mass end of the galaxy population.
  • The SFR gap between disks and bulge-dominated galaxies is only about 0.3 dex at $z \sim 0.2$, so most quenching of bulge-dominated systems must happen below $z < 0.3$, within roughly 2 Gyr.
  • G2's nearly constant mean stellar mass despite growing prominence, together with its higher Sérsic index at comparable effective radius, points to assembly by dissipative major mergers rather than in-situ star formation.
  • Intermediate-mass bins ($9.5 \le \log M_*/M_\odot < 10$) show nearly static morphological fractions, which can hide the transformation and explain why some surveys report weak morphological evolution.

Reading between the lines

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

  • If G1 truly persists as a star-forming, bulge-dominated population over the full 10 Gyr baseline, it may trace a formation channel — bulge growth through disk instabilities or pseudo-bulge assembly without quenching — that the merger narrative for ellipticals does not cover.
  • The paper's implication that most quenching happens recently ($z < 0.3$) is testable with local data: applying the same UV-based SFR calibration to low-redshift spectroscopic samples should show the disk-to-bulge SFR gap widening from about 0.3 dex toward the roughly 1 dex local value over the past ~2 Gyr.
  • The merger interpretation carries an independent check: if G2 is merger-built, independent merger indicators such as close-pair fractions or morphological disturbance rates at $z \sim 1$–$2$ should rise in lockstep with the G2 fraction.
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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 / 5 minor

Summary. This paper investigates the evolution of disk- and bulge-dominated galaxies using a CANDELS sample of ~14,000 galaxies selected with Hmag<=24, Mstellar>=1e9 Msun, and 0.2<=z<=2.4. Relying on the Kolesnikov et al. (2025) hybrid unsupervised-supervised morphological classification, the authors report that bulge-dominated galaxies develop a bimodal specific star formation rate (sSFR) distribution below z<1.6, whereas disks remain unimodal. A Gaussian mixture decomposition of the bulge sSFR distribution identifies two components: G1 (star-forming, lower mass, lower Sersic index) and G2 (quenched, more massive, higher Sersic index). The paper interprets these as two evolutionary tracks, with G2 formed through merger-driven transformations of massive disks, and presents mass-dependent evolution of morphological fractions as supporting evidence. The authors include caveats about SFR uncertainties and the indirect nature of the merger interpretation.

Significance. If the bimodality and the G1/G2 separation are real, this would be an observationally useful result: it would show that bulge-dominated galaxies are not a homogeneous population and that quenching and mass assembly may proceed along distinct paths. The paper has clear strengths: it uses an eye-free morphological classification, quantifies distribution shapes with detailed tables, includes an SFR-estimator comparison in Appendix A, and provides explicit caveats. However, the central claim is load-bearing on two under-supported choices: the adopted morphological classifier is not validated within this paper, and the two-component GMM is forced. Because G1 has exactly the properties expected of disk contaminants and the G1 weights are comparable to the stated misclassification rate, the uncertainty is not merely statistical. These issues need to be resolved before the main conclusion can be accepted.

major comments (3)
  1. [2.2.2 / Table B4] The adopted morphological classification is not validated within this paper, and the admitted upper limit on disk-to-bulge misclassification is of the same order as the G1 component. The paper states that without correction up to 18% of disks could be misclassified as bulge-dominated, but it does not report the post-correction contamination rate or provide a confusion matrix for the final classifier on an independent CANDELS sample. With 7,479 disks and 4,420 bulges, an 18% contamination would place ~1,346 true disks into the bulge catalogue, i.e., ~30% of it. The G1 weights in Table B4 are 0.31-0.52 for z<1.6, comparable to this contamination fraction, and G1's properties (high sSFR, lower stellar mass, lower Sersic index) match the properties expected of disk contaminants. To support the claim that G1 is a genuine bulge subpopulation, the authors should quantify the post-correction purity (e.g., via cross-field validation, comparison with visual classifications, or a confusion matrix on a held-out set) and, ideally, repeat the GMM analysis on a bulge sample restricted to the highest-confidence classifications.
  2. [4.1 / Appendix C] The choice of two Gaussian components is not used as a test of bimodality because the model selection is overridden. The text states that 'we decide to adopt n=2 for the GMM, irrespective of redshift' even when the AIC/BIC minimum lies at n!=2. The high-redshift bins (z>1.6) are explicitly noted as having weak bimodality, yet two components are still fitted and reported. To substantiate the claim that the sSFR distribution of bulge-dominated galaxies becomes bimodal below z<1.6, the paper should report the AIC/BIC values for n=1,2,3 in each redshift bin and apply a formal bimodality test (e.g., Hartigan's dip test or a calibrated likelihood-ratio). Without this, G1 and G2 are an assumed decomposition rather than an empirically supported separation.
  3. [Abstract / Section 2] The sample is described as 'mass-complete', but the selection also includes Hmag<=24 and no redshift-dependent completeness analysis is presented. A fixed magnitude cut will remove low-mass galaxies at high redshift, so the Mstellar>=1e9 limit may not be complete over the full 0.2<=z<=2.4 range. This could bias the apparent redshift growth of the massive G2 component and the morphological fraction evolution in Section 3.3 (Table 1). The authors should show the 90% stellar-mass completeness limit as a function of redshift and restrict the evolution analysis to the mass-redshift region where the sample is complete.
minor comments (5)
  1. [2.2.2] The phrase 'bulge-dominate' should be 'bulge-dominated'.
  2. [5] In the second bullet of the summary, 'galacies' should be 'galaxies'.
  3. [Figure 7] The labels 'BCSDSS', 'GVSDSS', and 'RSSDSS' are unclear; the caption should spell out that these are local-Universe boundaries from Trussler et al. (2020).
  4. [Table 1] For the high-mass bulge-dominated bin, the fitted C_s = 1.26 +/- 0.5 exceeds the physical bound of unity for a fraction; consider a bounded fit or an explicit caveat.
  5. [Appendix A] The sentence 'we consider only differences greater than 0.3 dex' is ambiguous; please clarify whether this refers to a threshold for trusting conclusions or a sample selection criterion.

Circularity Check

2 steps flagged · score 4.0 of 10

G2's 'quenched' label restates the GMM split; mass and Sérsic offsets supply the independent content.

  1. self definitional [Appendix B and Section 4.1]
    "By definition, G1 and G2 comprises, respectively, bulge-dominated galaxies with sSFR comparable and significant smaller than disks. ... We label the higher-sSFR component as G1 (magenta) and the lower-sSFR component as G2 (orange)."

    G2 is defined as the low-sSFR Gaussian component from a GMM fit to the sSFR distribution of bulge-dominated galaxies. Therefore any statement that G2 is 'quenched', 'less star-forming', or moves toward the green valley/red sequence (Sections 4.2 and 5, Abstract) is a restatement of that definitional split, not an independent measurement. The genuinely independent content is that G2 is more massive by 0.61 dex and has a higher Sérsic index by 0.99, neither of which is an input to the sSFR fit. Thus the 'quenched' tag is forced by construction, while the 'massive/concentrated' tag is not.

  2. self citation load bearing [Section 2.2.2]
    "To address this, we adopt the classification from Kolesnikov et al. (2025), which employs a hybrid, eye-free method."

    The entire disk/bulge sample split, which underpins the redshift-dependent bimodality and the subsequent G1/G2 decomposition, is taken from a prior paper by overlapping authors (Kolesnikov, Sampaio, de Carvalho). This paper does not independently validate the CANDELS classifier or provide a confusion matrix for the final labels; the claimed 'unbiased morphological classification' is asserted via self-citation rather than demonstrated here. This is a load-bearing dependency on the authors' own prior work, though it is a data-input dependency rather than a logical identity, so it weakens but does not by itself force the central result.

full rationale

The paper's central claim—that bulge-dominated galaxies separate into a star-forming G1 and a quenched, massive G2—is only partly circular. The GMM decomposition is performed on sSFR, and G2 is defined as the lower-sSFR component, so calling G2 'quenched' or 'less star-forming' is definitionally true. However, the paper does provide independent, non-input evidence: G2 is more massive by 0.61 dex and more centrally concentrated by 0.99 in Sérsic index, and its GMM weight grows toward lower redshift. These trends are not encoded in the sSFR fit. A secondary concern is that the disk/bulge classification itself comes from Kolesnikov et al. (2025) by the same group, and the final classifier is not independently validated in this paper; this makes the input load-bearing but not identical to the output. Overall, the 'quenched' descriptor is circular by construction, but the physical distinction between G1 and G2 retains substantial independent content, so the paper is not fundamentally circular.

Assumptions & free parameters 3 free parameters · 5 assumptions · 1 invented entities

The central claims rest on an external morphology classifier, an imposed two-component GMM, a magnitude-selected sample described as mass-complete, and SED-based SFRs; none of these are derived in this paper.

free parameters (3)
  • GMM component parameters for bulge-dominated sSFR (G1 and G2 means, sigmas, weights per redshift bin) = Table B4: z=0.2-0.4 mu1=-9.56, mu2=-10.84, sigma1=0.47, sigma2=0.31, w1=0.44, w2=0.56; values vary with redshift
    The central bimodality claim rests on this fitted decomposition; the components are not independently predicted.
  • Number of Gaussian components n = 2, chosen for every redshift bin
    Appendix C states n=2 is adopted irrespective of redshift even when the minimum AIC/BIC occurs at n != 2.
  • Power-law fraction fit parameters C and m for disk and bulge fractions = Table 1, e.g. highest mass disk bin C=0.05+/-0.04, m=2.04+/-0.03
    Used to quantify the mass-dependent morphological transition; descriptive fit, not a prediction.
assumptions (5)
  • domain assumption Morphological labels from Kolesnikov et al. (2025) are accurate across 0.2 <= z <= 2.4.
    All conclusions are conditioned on these labels; the paper relies on the companion paper and does not independently validate final classification accuracy.
  • ad hoc to paper A two-component Gaussian mixture adequately represents the bulge-dominated sSFR distribution at all redshifts.
    Appendix C: n=2 is adopted irrespective of redshift even when AIC/BIC do not select n=2.
  • domain assumption The H_mag <= 24 plus M_stellar >= 1e9 Msun selection is mass complete over the full redshift range.
    Selection is by magnitude, not mass; no completeness correction is presented, so missing faint quiescent bulges at high z could mimic the growth of G2.
  • domain assumption SFR_UV,corr from Barro et al. (2019) traces total star formation with no redshift-dependent bias.
    Appendix A shows about 0.3 dex scatter versus UV+IR for 5,143 galaxies but does not test whether the sSFR bimodality survives with FIR-based SFRs.
  • domain assumption The local Universe blue cloud, green valley, and red sequence boundaries approximately apply at all redshifts when assigning G2 galaxies.
    Section 4.2 uses fixed local boundaries from Trussler et al. (2020) and notes they may shift with redshift, so the quoted 17%, 49%, and 34% fractions are only approximate.
invented entities (1)
  • G1 and G2 bulge-dominated subpopulations
    purpose: Organize the bimodal sSFR distribution and support two divergent evolutionary tracks.
    They are Gaussian components fit to the same sSFR distribution used to define the bimodality; the mass and Sersic-index differences are examined within the same sample, not on independent data.

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Cite this review

Pith. "Pith review of Morphology across cosmic time: assessing the evolution and interplay of disk and bulge-dominated galaxies in the CANDELS survey." pith.science (2026). https://pith.science/paper/7PFPVQZA

@misc{pith2026250612205,
  author       = {Pith},
  title        = {Pith review of: Morphology across cosmic time: assessing the evolution and interplay of disk and bulge-dominated galaxies in the CANDELS survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7PFPVQZA}},
  note         = {Machine review of arXiv:2506.12205}
}
abstract

We investigate the redshift evolution of disk and bulge-dominated galaxies using a mass-complete sample of $\sim$14,000 galaxies from the CANDELS survey, selected with $H_{\rm mag} \leq 24$, $M_{\rm stellar} \geq 10^9\,{\rm M}_\odot$, and spanning $0.2 \leq z \leq 2.4$. Adopting an unbiased morphological classification, free from visual inspection or parametric assumptions, we explore the evolution of specific star formation rate (sSFR), stellar mass, structural properties, and galaxy fractions as a function of redshift and morphology. We find that while disk and bulge-dominated galaxies exhibit similar sSFR distributions at $z \sim 2.4$, bulge-dominated systems develop a redshift-dependent bimodality below $z < 1.6$, unlike the unimodal behaviour of disks. This bimodality correlates with stellar mass: bulge-dominated galaxies with lower sSFR are significantly more massive and exhibit higher S\'ersic indices than their star-forming counterparts, despite having similar effective radii. Based on a Gaussian mixture decomposition, we identify two evolutionary tracks for bulge-dominated galaxies: G1, a long-lived, star-forming population with disk-like properties; and G2, a quenched, massive population whose prominence increases with decreasing redshift. The evolution of the star formation main sequence and morphology--mass fractions support a scenario in which G2 systems form through merger-driven transformations of massive disks. Our results indicate that bulge-dominated galaxies are not a homogeneous population, but instead follow divergent evolutionary paths driven by distinct physical mechanisms.

Figures

Figures reproduced from arXiv: 2506.12205 by the authors.

Figure 1
Figure 1. Distribution of sSFR for disk and bulge-dominated galaxies. Each panel shows the distribution for a given redshift bin. Dotted lines represent the histograms, whereas the solid lines show an adaptive kernel density estimate, applied to highlight overall trends. In each panel, we also include the number of galaxies in each morphological class in the legend. Despite starting from roughly the same sSFR distribution in … view at source ↗
Figure 2
Figure 2. Analogue to [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Similar to [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Fraction of disk (blue colored) and bulge-dominated (red colored) galaxies as a function of redshift, separating systems into four stellar mass bins. Shaded areas denote the uncertainty in the fraction of each bin, and is calculated by assuming a multinomial distributi…
Figure 5
Figure 5. Figure 5: Decomposition of the observed sSFR distribution of bulge-dominated galaxies into two Gaussian components, with each panel representing a different redshift bin. Background histogram in red shows the observed distributions, the red solid line shows an adaptive kernel de…
Figure 6
Figure 6. Figure 6 [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Distributions of G1 (magenta) and G2 (orange) in the SFMS diagram. Whereas scatter points represent the observed data, the contour lines (also in magenta and orange) traces the data density distribution as a function of location in the diagram. For completeness, and to…
Figure 8
Figure 8. Figure 8: Contour lines of the G1 (magenta) and G2 (orange) distributions in the 𝑛s vs. 𝑅e diagram, with each panel representing a different redshift bin. ACKNOWLEDGEMENTS The authors acknowledge the comments of the anonymous referee, that significantly helped improving this wor…

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Forward citations

Cited by 1 Pith paper

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

  1. Morphologies of SAGAbg low-mass galaxies in Legacy Survey multi-band imaging: dependence on stellar masses, star-formation rates and low-redshift evolution

    astro-ph.GA 2026-07 conditional novelty 4.0 of 10

    Low-mass star-forming galaxies are disk-dominated; their light concentration increases with stellar mass and decreases with sSFR, with bulges emerging near log(M*/M_sun) ~ 9.

Reference graph

Works this paper leans on

91 extracted references · 16 canonical work pages · cited by 1 Pith paper

  1. [1]

    write newline

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

  2. [2]

    Angthopo J., Ferreras I., Silk J., 2019, @doi [ ] 10.1093/mnrasl/slz106 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488L..99A 488, L99

  3. [3]

    H., et al., 2020, @doi [Astronomy and Computing] 10.1016/j.ascom.2019.100334 , https://ui.adsabs.harvard.edu/abs/2020A&C....3000334B 30, 100334

    Barchi P. H., et al., 2020, @doi [Astronomy and Computing] 10.1016/j.ascom.2019.100334 , https://ui.adsabs.harvard.edu/abs/2020A&C....3000334B 30, 100334

  4. [4]

    Barden M., Jahnke K., H \"a u ler B., 2008, The Astrophysical Journal Supplement Series, 175, 105

  5. [5]

    Barro G., et al., 2013, @doi [ ] 10.1088/0004-637X/765/2/104 , https://ui.adsabs.harvard.edu/abs/2013ApJ...765..104B 765, 104

  6. [6]

    Barro G., et al., 2017, @doi [ ] 10.3847/1538-4357/aa6b05 , https://ui.adsabs.harvard.edu/abs/2017ApJ...840...47B 840, 47

  7. [7]

    Barro G., et al., 2019, @doi [ ] 10.3847/1538-4365/ab23f2 , https://ui.adsabs.harvard.edu/abs/2019ApJS..243...22B 243, 22

  8. [8]

    F., Phleps S., Somerville R

    Bell E. F., Phleps S., Somerville R. S., Wolf C., Borch A., Meisenheimer K., 2006, @doi [ ] 10.1086/508408 , https://ui.adsabs.harvard.edu/abs/2006ApJ...652..270B 652, 270

Show all 91 references
  1. [9]

    F., et al., 2012, @doi [ ] 10.1088/0004-637X/753/2/167 , https://ui.adsabs.harvard.edu/abs/2012ApJ...753..167B 753, 167

    Bell E. F., et al., 2012, @doi [ ] 10.1088/0004-637X/753/2/167 , https://ui.adsabs.harvard.edu/abs/2012ApJ...753..167B 753, 167

  2. [10]

    A., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2012.22087.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427.1666B 427, 1666

    Bruce V. A., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2012.22087.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427.1666B 427, 1666

  3. [11]

    Bruzual G., Charlot S., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06897.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.344.1000B 344, 1000

  4. [12]

    S., Conselice C

    Bundy K., Ellis R. S., Conselice C. J., 2005, @doi [ ] 10.1086/429549 , https://ui.adsabs.harvard.edu/abs/2005ApJ...625..621B 625, 621

  5. [13]

    Ceverino D., Dekel A., Tweed D., Primack J., 2015, @doi [ ] 10.1093/mnras/stu2694 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.447.3291C 447, 3291

  6. [14]

    Cheng T.-Y., et al., 2023, Monthly Notices of the Royal Astronomical Society, 518, 2794

  7. [15]

    Cambridge University Press

    Cimatti A., Fraternali F., Nipoti C., 2019, Introduction to Galaxy Formation and Evolution: From Primordial Gas to Present-Day Galaxies . Cambridge University Press

  8. [16]

    G., 2018, @doi [ ] 10.1093/mnras/sty1229 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.478.3994C 478, 3994

    Clauwens B., Schaye J., Franx M., Bower R. G., 2018, @doi [ ] 10.1093/mnras/sty1229 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.478.3994C 478, 3994

  9. [17]

    J., 2003, The Astrophysical Journal Supplement Series, 147, 1

    Conselice C. J., 2003, The Astrophysical Journal Supplement Series, 147, 1

  10. [18]

    J., 2007, in Combes F., Palou s J., eds, IAU Symposium Vol

    Conselice C. J., 2007, in Combes F., Palou s J., eds, IAU Symposium Vol. 235, Galaxy Evolution across the Hubble Time. pp 381--384 ( @eprint arXiv astro-ph/0610662 ), @doi 10.1017/S1743921306010222

  11. [19]

    Conselice C., Bluck A., Ravindranath S., Mortlock A., Koekemoer A., Buitrago F., Gr \"u tzbauch R., Penny S., 2011, Monthly Notices of the Royal Astronomical Society, 417, 2770

  12. [20]

    Davis M., et al., 2007, @doi [ ] 10.1086/517931 , https://ui.adsabs.harvard.edu/abs/2007ApJ...660L...1D 660, L1

  13. [21]

    Dom \' nguez S \'a nchez H., Huertas-Company M., Bernardi M., Tuccillo D., Fischer J., 2018, Monthly Notices of the Royal Astronomical Society, 476, 3661

  14. [22]

    Dubois Y., Gavazzi R., Peirani S., Silk J., 2013, @doi [ ] 10.1093/mnras/stt997 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.433.3297D 433, 3297

  15. [23]

    Estrada-Carpenter V., et al., 2023, @doi [ ] 10.3847/1538-4357/acd4be , https://ui.adsabs.harvard.edu/abs/2023ApJ...951..115E 951, 115

  16. [24]

    Faber S., 2011, MAST, doi, 10, T94S3X

  17. [25]

    R., Trevisan M., 2015, The Astrophysical Journal, 814, 55

    Ferrari F., de Carvalho R. R., Trevisan M., 2015, The Astrophysical Journal, 814, 55

  18. [26]

    Ferreira L., et al., 2022, @doi [ ] 10.3847/2041-8213/ac947c , https://ui.adsabs.harvard.edu/abs/2022ApJ...938L...2F 938, L2

  19. [27]

    C., et al., 1998, in Bely P

    Ford H. C., et al., 1998, in Bely P. Y., Breckinridge J. B., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 3356, Space Telescopes and Instruments V. pp 234--248, @doi 10.1117/12.324464

  20. [28]

    Fu H., et al., 2013, @doi [ ] 10.1038/nature12184 , https://ui.adsabs.harvard.edu/abs/2013Natur.498..338F 498, 338

  21. [29]

    Giavalisco M., et al., 2004, @doi [ ] 10.1086/379232 , https://ui.adsabs.harvard.edu/abs/2004ApJ...600L..93G 600, L93

  22. [30]

    A., et al., 2011, The Astrophysical Journal Supplement Series, 197, 35

    Grogin N. A., et al., 2011, The Astrophysical Journal Supplement Series, 197, 35

  23. [31]

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

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

  24. [32]

    H \"a u ler B., et al., 2013, @doi [ ] 10.1093/mnras/sts633 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.430..330H 430, 330

  25. [33]

    H \"a u ler B., et al., 2022, @doi [ ] 10.1051/0004-6361/202142935 , https://ui.adsabs.harvard.edu/abs/2022A&A...664A..92H 664, A92

  26. [34]

    F., Hernquist L., Cox T

    Hopkins P. F., Hernquist L., Cox T. J., Di Matteo T., Robertson B., Springel V., 2006, @doi [ ] 10.1086/499298 , https://ui.adsabs.harvard.edu/abs/2006ApJS..163....1H 163, 1

  27. [35]

    F., Cox T

    Hopkins P. F., Cox T. J., Dutta S. N., Hernquist L., Kormendy J., Lauer T. R., 2009, @doi [ ] 10.1088/0067-0049/181/1/135 , https://ui.adsabs.harvard.edu/abs/2009ApJS..181..135H 181, 135

  28. [36]

    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

  29. [37]

    G., Baugh C

    Hu s ko F., Lacey C. G., Baugh C. M., 2023, @doi [ ] 10.1093/mnras/stac3152 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.5323H 518, 5323

  30. [38]

    S., et al., 2015, The Astrophysical Journal Supplement Series, 221, 11

    Kartaltepe J. S., et al., 2015, The Astrophysical Journal Supplement Series, 221, 11

  31. [39]

    M., et al., 2011, The Astrophysical Journal Supplement Series, 197, 36

    Koekemoer A. M., et al., 2011, The Astrophysical Journal Supplement Series, 197, 36

  32. [40]

    Elsevier, pp 981--990

    Kohonen T., 1991, in , Artificial neural networks. Elsevier, pp 981--990

  33. [41]

    M., de Carvalho R

    Kolesnikov I., Sampaio V. M., de Carvalho R. R., Conselice C., Rembold S. B., Mendes C. L., Rosa R. R., 2024, @doi [ ] 10.1093/mnras/stad3934 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528...82K 528, 82

  34. [42]

    M., de Carvalho R

    Kolesnikov I., Sampaio V. M., de Carvalho R. R., Conselice C., 2025, @doi [ ] 10.1093/mnras/staf625 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.tmp..593K

  35. [43]

    Lawrence A., et al., 2007, @doi [ ] 10.1111/j.1365-2966.2007.12040.x , https://ui.adsabs.harvard.edu/abs/2007MNRAS.379.1599L 379, 1599

  36. [44]

    M., Primack J., Madau P., 2004, The Astronomical Journal, 128, 163

    Lotz J. M., Primack J., Madau P., 2004, The Astronomical Journal, 128, 163

  37. [45]

    M., et al., 2008, @doi [ ] 10.1086/523659 , https://ui.adsabs.harvard.edu/abs/2008ApJ...672..177L 672, 177

    Lotz J. M., et al., 2008, @doi [ ] 10.1086/523659 , https://ui.adsabs.harvard.edu/abs/2008ApJ...672..177L 672, 177

  38. [46]

    Madau P., Dickinson M., 2014, @doi [ ] 10.1146/annurev-astro-081811-125615 , https://ui.adsabs.harvard.edu/abs/2014ARA&A..52..415M 52, 415

  39. [47]

    Martig M., Bournaud F., Teyssier R., Dekel A., 2009, @doi [ ] 10.1088/0004-637X/707/1/250 , https://ui.adsabs.harvard.edu/abs/2009ApJ...707..250M 707, 250

  40. [48]

    McKinney W., et al., 2010, @doi [Proceedings of the 9th Python in Science Conference] 10.25080/Majora-92bf1922-00a , 445, 51

  41. [49]

    Mortlock A., et al., 2013, Monthly Notices of the Royal Astronomical Society, 433, 1185

  42. [50]

    H., Ostriker J

    Naab T., Johansson P. H., Ostriker J. P., 2009, @doi [ ] 10.1088/0004-637X/699/2/L178 , https://ui.adsabs.harvard.edu/abs/2009ApJ...699L.178N 699, L178

  43. [51]

    V., et al., 2024, @doi [ ] 10.1093/mnras/stae1702 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.532.3747N 532, 3747

    Nedkova K. V., et al., 2024, @doi [ ] 10.1093/mnras/stae1702 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.532.3747N 532, 3747

  44. [52]

    C., Dekel A., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10918.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.372..933N 372, 933

    Neistein E., van den Bosch F. C., Dekel A., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10918.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.372..933N 372, 933

  45. [53]

    J., et al., 2016, @doi [ ] 10.3847/0004-637X/828/1/27 , https://ui.adsabs.harvard.edu/abs/2016ApJ...828...27N 828, 27

    Nelson E. J., et al., 2016, @doi [ ] 10.3847/0004-637X/828/1/27 , https://ui.adsabs.harvard.edu/abs/2016ApJ...828...27N 828, 27

  46. [54]

    P., Gon c alves T

    Nogueira-Cavalcante J. P., Gon c alves T. S., Men \'e ndez-Delmestre K., de la Rosa I. G., Charbonnier A., 2019, @doi [ ] 10.1093/mnras/stz190 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.3022N 484, 3022

  47. [55]

    P., Naab T., Johansson P

    Oser L., Ostriker J. P., Naab T., Johansson P. H., Burkert A., 2010, @doi [ ] 10.1088/0004-637X/725/2/2312 , https://ui.adsabs.harvard.edu/abs/2010ApJ...725.2312O 725, 2312

  48. [56]

    Pandya V., et al., 2017, @doi [ ] 10.1093/mnras/stx2027 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472.2054P 472, 2054

  49. [57]

    Papovich C., et al., 2018, @doi [ ] 10.3847/1538-4357/aaa766 , https://ui.adsabs.harvard.edu/abs/2018ApJ...854...30P 854, 30

  50. [58]

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

  51. [59]

    Peebles P. J. E., 1969, @doi [ ] 10.1086/149876 , https://ui.adsabs.harvard.edu/abs/1969ApJ...155..393P 155, 393

  52. [60]

    Planck Collaboration et al., 2016, @doi [ ] 10.1051/0004-6361/201525830 , https://ui.adsabs.harvard.edu/abs/2016A&A...594A..13P 594, A13

  53. [61]

    Popesso P., et al., 2023, @doi [ ] 10.1093/mnras/stac3214 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.1526P 519, 1526

  54. [62]

    Prieto M., et al., 2013, @doi [ ] 10.1093/mnras/sts065 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428..999P 428, 999

  55. [63]

    L., Patton D

    Quai S., Byrne-Mamahit S., Ellison S. L., Patton D. R., Hani M. H., 2023, @doi [ ] 10.1093/mnras/stac3713 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.2119Q 519, 2119

  56. [64]

    Rodriguez-Gomez V., et al., 2016, @doi [ ] 10.1093/mnras/stw456 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.458.2371R 458, 2371

  57. [65]

    Rosa R., et al., 2018, Monthly Notices of the Royal Astronomical Society: Letters, 477, L101

  58. [66]

    M., de Carvalho R

    Sampaio V. M., de Carvalho R. R., Ferreras I., Arag \'o n-Salamanca A., Parker L. C., 2022, @doi [ ] 10.1093/mnras/stab3018 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.509..567S 509, 567

  59. [67]

    M., Arag \'o n-Salamanca A., Merrifield M

    Sampaio V. M., Arag \'o n-Salamanca A., Merrifield M. R., de Carvalho R. R., Zhou S., Ferreras I., 2023, @doi [ ] 10.1093/mnras/stad2211 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.5327S 524, 5327

  60. [68]

    Santini P., et al., 2015, @doi [ ] 10.1088/0004-637X/801/2/97 , https://ui.adsabs.harvard.edu/abs/2015ApJ...801...97S 801, 97

  61. [69]

    Scarlata C., et al., 2007, @doi [ ] 10.1086/517972 , https://ui.adsabs.harvard.edu/abs/2007ApJS..172..494S 172, 494

  62. [70]

    Schawinski K., et al., 2009, @doi [ ] 10.1111/j.1365-2966.2009.14793.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.396..818S 396, 818

  63. [71]

    Schawinski K., et al., 2014, @doi [ ] 10.1093/mnras/stu327 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.440..889S 440, 889

  64. [72]

    Scoville N., et al., 2007, @doi [ ] 10.1086/516585 , https://ui.adsabs.harvard.edu/abs/2007ApJS..172....1S 172, 1

  65. [73]

    S., Steinhardt C

    Speagle J. S., Steinhardt C. L., Capak P. L., Silverman J. D., 2014, @doi [ ] 10.1088/0067-0049/214/2/15 , https://ui.adsabs.harvard.edu/abs/2014ApJS..214...15S 214, 15

  66. [74]

    Springel V., Hernquist L., 2005, @doi [ ] 10.1086/429486 , https://ui.adsabs.harvard.edu/abs/2005ApJ...622L...9S 622, L9

  67. [75]

    Tacchella S., et al., 2022, @doi [ ] 10.3847/1538-4357/ac4cad , https://ui.adsabs.harvard.edu/abs/2022ApJ...927..170T 927, 170

  68. [76]

    Talia M., Cimatti A., Mignoli M., Pozzetti L., Renzini A., Kurk J., Halliday C., 2014, Astronomy & Astrophysics, 562, A113

  69. [77]

    F., Remus R.-S., Dolag K., Beck A

    Teklu A. F., Remus R.-S., Dolag K., Beck A. M., Burkert A., Schmidt A. S., Schulze F., Steinborn L. K., 2015, @doi [ ] 10.1088/0004-637X/812/1/29 , https://ui.adsabs.harvard.edu/abs/2015ApJ...812...29T 812, 29

  70. [78]

    R., et al., 2016, @doi [ ] 10.3847/0004-637X/817/2/118 , https://ui.adsabs.harvard.edu/abs/2016ApJ...817..118T 817, 118

    Tomczak A. R., et al., 2016, @doi [ ] 10.3847/0004-637X/817/2/118 , https://ui.adsabs.harvard.edu/abs/2016ApJ...817..118T 817, 118

  71. [79]

    Treu T., et al., 2005, @doi [ ] 10.1086/444585 , https://ui.adsabs.harvard.edu/abs/2005ApJ...633..174T 633, 174

  72. [80]

    Trussler J., Maiolino R., Maraston C., Peng Y., Thomas D., Goddard D., Lian J., 2020, @doi [ ] 10.1093/mnras/stz3286 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.5406T 491, 5406

  73. [81]

    P., H \"a u ler B., Rojas A

    Vika M., Bamford S. P., H \"a u ler B., Rojas A. L., Borch A., Nichol R. C., 2013, @doi [ ] 10.1093/mnras/stt1320 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.435..623V 435, 623

  74. [82]

    P., H \"a u ler B., Rojas A

    Vika M., Bamford S. P., H \"a u ler B., Rojas A. L., 2014, @doi [ ] 10.1093/mnras/stu1696 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.444.3603V 444, 3603

  75. [83]

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

  76. [84]

    Walmsley M., et al., 2022, Monthly Notices of the Royal Astronomical Society, 509, 3966

  77. [85]

    Walmsley M., et al., 2023, Monthly Notices of the Royal Astronomical Society, 526, 4768

  78. [86]

    Weinzirl T., et al., 2011, @doi [ ] 10.1088/0004-637X/743/1/87 , https://ui.adsabs.harvard.edu/abs/2011ApJ...743...87W 743, 87

  79. [87]

    E., van Dokkum P

    Whitaker K. E., van Dokkum P. G., Brammer G., Franx M., 2012, @doi [ ] 10.1088/2041-8205/754/2/L29 , https://ui.adsabs.harvard.edu/abs/2012ApJ...754L..29W 754, L29

  80. [88]

    E., et al., 2015, @doi [ ] 10.1088/2041-8205/811/1/L12 , https://ui.adsabs.harvard.edu/abs/2015ApJ...811L..12W 811, L12

    Whitaker K. E., et al., 2015, @doi [ ] 10.1088/2041-8205/811/1/L12 , https://ui.adsabs.harvard.edu/abs/2015ApJ...811L..12W 811, L12

  81. [89]

    G., et al., 2000, @doi [ ] 10.1086/301513 , https://ui.adsabs.harvard.edu/abs/2000AJ....120.1579Y 120, 1579

    York D. G., et al., 2000, @doi [ ] 10.1086/301513 , https://ui.adsabs.harvard.edu/abs/2000AJ....120.1579Y 120, 1579

  82. [90]

    de S \'a -Freitas C., et al., 2022, @doi [ ] 10.1093/mnras/stab3230 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.3889D 509, 3889

  83. [91]

    G., et al., 2013, @doi [ ] 10.1088/2041-8205/771/2/L35 , https://ui.adsabs.harvard.edu/abs/2013ApJ...771L..35V 771, L35

    van Dokkum P. G., et al., 2013, @doi [ ] 10.1088/2041-8205/771/2/L35 , https://ui.adsabs.harvard.edu/abs/2013ApJ...771L..35V 771, L35

Pith tools

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