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JWST color cuts miss most of the truly massive high-redshift galaxies, and those galaxies rarely become today's giants without late mergers.

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Observational JWST/NIRCam selections recover almost none of TNG300's M⋆≥10^11 M⊙ galaxies at z~5 and their descendants rarely become the most massive systems at z=0 unless they experience late merger growth.

T0 review reviewed 2026-07-13 challenge →

load-bearing objection Solid simulation test of published NIRCam cuts: they miss most of the true high-mass end at z~5 in TNG300, and early mass rank is a poor predictor of z=0 rank unless late mergers intervene. the 2 major comments →

arxiv 2607.08820 v1 pith:LAIRACSP submitted 2026-07-09 astro-ph.GA

What becomes of JWST/NIRCam-selected high-redshift massive galaxies?

Devontae C. Baxter , Alison L. Coil , Ethan O Nadler , Xuejian Shen , Mark Vogelsberger This is my paper
classification astro-ph.GA
keywords high-redshift galaxiesJWST/NIRCamphotometric selectiongalaxy formationIllustrisTNGdust attenuationstellar mass assemblyprogenitors of massive galaxies
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

Early JWST/NIRCam surveys found many high-redshift massive-galaxy candidates that earlier UV surveys missed, raising questions about whether galaxy formation models are incomplete and whether those objects are the direct ancestors of today's most massive galaxies. This paper tests five published photometric color selections by applying them to galaxies in the large-volume TNG300 simulation that have been given synthetic dust-attenuated NIRCam photometry. The most inclusive published cut still recovers only one of the eighteen simulated galaxies that already exceed 10^11 solar masses at z ~ 5; the rest are systematically bluer under the adopted dust model. The authors therefore introduce magnitude-dependent color cuts that better isolate massive systems while rejecting dusty low-mass interlopers. Tracking the selected galaxies forward in the merger trees shows that they almost never finish as the most massive galaxies at z = 0. Only those that experience substantial merger-driven growth at very late times (z less than or equal to 0.2) join the ranks of the present-day giants. The work therefore both improves how observers should select high-redshift massive galaxies and warns against reading today's most massive systems as simple descendants of the JWST candidates.

Core claim

Published JWST/NIRCam color selections do not recover the most massive high-redshift galaxies in the TNG300 simulation (only 1 of 18 systems with stellar mass above 10^11 solar masses at z ~ 5 satisfies the best cut), and the galaxies that are selected rarely evolve into the most massive galaxies by z = 0 unless they undergo substantial late-time merger growth.

What carries the argument

Synthetic dust-attenuated JWST/NIRCam photometry for TNG300 galaxies (full Monte-Carlo radiative transfer) combined with SUBLINK merger trees that link each high-redshift selected galaxy to its z = 0 descendant.

Load-bearing premise

The dust model underestimates how much ultraviolet light is blocked in the most massive high-redshift galaxies, so the true number of systems that would pass the color cuts is higher than the simulation reports.

What would settle it

If deeper multi-band imaging or spectroscopy of a complete mass-selected sample at z ~ 5 shows that the majority of galaxies above 10^11 solar masses already satisfy the published red color cuts (or that their descendants systematically rank among the top ten most massive systems at z = 0 without late mergers), the central claims would be contradicted.

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

If this is right

  • Observers should replace single-color cuts with magnitude-dependent Balmer-break selections if they want higher purity and completeness for massive z ~ 5 galaxies.
  • Number-density comparisons between JWST candidates and theoretical models must treat the published selections as incomplete at the high-mass end.
  • Searches for progenitors of brightest cluster galaxies cannot rely solely on the most massive high-redshift galaxies inside overdensities.
  • Claims that a given high-redshift massive galaxy is a direct progenitor of a present-day ultra-massive galaxy require independent evidence of late-time merger activity.

Where Pith is reading between the lines

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

  • If the dust underestimation is as large as the UV-slope discrepancy suggests, many of the observed 'little red dots' and extremely red candidates may still be lower-mass or AGN-contaminated systems once better dust models are applied.
  • Wide-area cosmic-web maps at z > 2 (e.g., from future infrared surveys) could supply the environmental context needed to flag which massive high-redshift galaxies are most likely to experience the late mergers that produce today's giants.
  • The same simulation-based selection-refinement method can be repeated at z = 7 and z = 4 to produce redshift-specific purity cuts for ongoing deep fields.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 5 minor

Summary. The paper applies five published JWST/NIRCam color–magnitude selections to TNG300 galaxies with synthetic dust-attenuated photometry (Model C of Vogelsberger et al. 2020 / Shen et al. 2020) and finds that the Pérez-González et al. (2023) cut (S1) is the most inclusive among those that recover any objects. Even so, only 1 of 18 galaxies with M⋆ ≥ 10^11 M⊙ at z ∼ 5 satisfies S1; the rest are ∼0.5 mag bluer under the adopted dust model. The authors introduce magnitude-dependent S6/S7 wedges in (m200W − m277W) vs m150W that improve purity/completeness for M⋆ > 10^10.5 M⊙ at z = 5 while excluding low-mass dusty contaminants. Using SUBLINK merger trees they then show that the most massive NIRCam-selected (and purely mass-selected) galaxies at z = 7, 5, 4 and 2 rarely remain among the most massive systems at z = 0; only those that experience substantial late-time (z ≲ 0.2) merger-driven growth do so. The work therefore cautions both that current observational NIRCam cuts miss the most massive high-z galaxies and that high-z mass rank is a poor predictor of z = 0 mass rank.

Significance. If the incompleteness and late-time-merger results hold, the paper supplies a concrete, simulation-grounded caution for interpreting JWST photometric massive-galaxy candidates as either a complete census of the high-mass end or as direct progenitors of present-day BCGs/cluster centrals. Strengths include the transparent purity/completeness optimization that yields the new S6/S7 wedges, the explicit 1/18 recovery statistic, the multi-redshift growth-history comparison (Figs. 7–9), and the authors’ own quantification of the dust-model bias as a lower-limit statement (§5.3). The analysis is cleanly executed inside a single, well-documented simulation and is immediately useful to observers designing follow-up selections.

major comments (2)
  1. §5.3 and top-right panel of Fig. 2: the central incompleteness claim (1 of 18 galaxies with M⋆ ≥ 10^11 M⊙ recovered by S1) is load-bearing, yet the paper itself states that Model C underestimates UV attenuation for bright/massive systems (steeper simulated β–MUV at z = 4–6). While the authors correctly label the recovery fraction a lower limit, the manuscript never quantifies how large a reddening shift would be required to move the remaining 17 objects into the S1 wedge, nor whether that shift would also pull low-mass dusty satellites into the same region. A short sensitivity test (e.g., uniform or mass-dependent ΔE(B−V) or Δβ applied to the high-mass tail) is needed before the absolute statement “current selections are not identifying the most massive high-redshift galaxies” can be taken at face value outside TNG300+Model C.
  2. §3.3 and Fig. 4: S6/S7 are optimized on the same TNG300 photometry to which they are then applied; purity (0.70) and completeness (0.39/0.88) are therefore in-sample figures of merit. The paper presents them as “improved” observational selections, but without a hold-out redshift, an independent simulation, or an explicit statement that they remain untested against low-z interlopers (already noted in the text), their claimed superiority for real JWST catalogs is not yet demonstrated. Either a cross-validation step or a clearer framing as “simulation-motivated proposals requiring observational validation” is required.
minor comments (5)
  1. Abstract and §3.1: the abstract says “1 of the 18 galaxies” while the body text sometimes says “1 of the 17”; reconcile the exact count of M⋆ ≥ 10^11 M⊙ systems at z = 5.
  2. Table 1 / Fig. 1: S4 and S5 return zero objects even at z > 5; a one-sentence quantitative statement of how far the simulated colors fall short of the published cuts would help readers judge whether the non-detection is decisive or merely a dust-model artifact.
  3. Fig. 6 caption: the dynamic-range statement is useful, but the physical scale bars (0.5 cMpc) are hard to read in the rendered panels; enlarge or move them.
  4. §2.2: the redshift-dependent dust-to-metal ratio 0.9 × (z/2)^−1.92 is given without an immediate citation to the calibration paper; add the reference for reproducibility.
  5. Throughout: “NIRCam-selected” is used both for the observational S1–S5 cuts and for the new S6/S7 wedges; a brief terminological distinction (e.g., “literature-selected” vs “simulation-optimized”) would reduce ambiguity.

Circularity Check

0 steps flagged

No significant circularity: observational cuts are external tests applied to an independent simulation; new wedges are openly simulation-optimized proposals, not forced predictions; descendant tracking is direct SUBLINK output.

full rationale

The paper's load-bearing claims are (1) that five published NIRCam color-magnitude cuts recover only a minority (or zero) of TNG300's M⋆≥10^11 M⊙ galaxies at z=5 under the adopted dust-attenuated photometry, and (2) that the most massive high-z galaxies (whether mass-selected or NIRCam-selected) rarely remain among the most massive at z=0 unless they experience substantial late-time (z≲0.2) merger growth. Both are obtained by direct application of external observational criteria (Table 1) to the pre-existing TNG300 + SKIRT photometry catalog and by following SUBLINK main-descendant branches; neither claim is defined in terms of the other, nor is any free parameter fitted to the target quantity and then re-labeled a prediction. The new S6/S7 wedges are explicitly constructed by maximizing purity/completeness on the same simulation sample and are presented as simulation-motivated proposals for observers, not as independent forecasts. Self-citations to Vogelsberger et al. (2020) and Shen et al. (2020) supply the photometry product and are accompanied by an explicit discussion of the model's known under-attenuation of UV light for bright galaxies (§5.3); that caveat is treated as making the recovery fraction a lower limit rather than as a uniqueness theorem that forces the conclusions. No equation reduces a claimed result to its input by construction, and no uniqueness or ansatz is imported solely via overlapping-author citation. The analysis is therefore self-contained against the simulation's own outputs.

Axiom & Free-Parameter Ledger

3 free parameters · 3 axioms · 1 invented entities

The analysis rests on the public TNG300 galaxy-formation model, a published dust RT post-processing pipeline, and standard SUBFIND/SUBLINK definitions; the only free parameters that directly affect the central incompleteness claim are those inside the dust model.

free parameters (3)
  • redshift-dependent dust-to-metal ratio = 0.9×(z/2)^−1.92
    Set to 0.9×(z/2)^−1.92 and calibrated to observed UV luminosity functions at z=2–10; directly controls the amount of reddening that decides which massive galaxies enter the color cuts.
  • birth-cloud age threshold for MAPPINGS-III replacement = 10 Myr
    Stars younger than 10 Myr have their FSPS templates replaced by MAPPINGS-III to model unresolved birth-cloud attenuation; choice affects UV colors of the most actively star-forming systems.
  • aperture for flux integration = 30 pkpc
    Fixed 30 pkpc physical aperture centered on each galaxy; truncates extended light and can alter magnitudes of the most massive systems.
axioms (3)
  • domain assumption TNG300 baryonic physics (star formation, dual-mode AGN feedback, stellar winds) correctly ranks galaxies by stellar mass at z=5 and z=0
    All mass rankings and descendant statements inherit the simulation’s calibrated sub-grid model; invoked throughout §§2–4.
  • domain assumption Dust traces cold, star-forming gas metals with a spatially constant dust-to-metal ratio
    Core assumption of the SKIRT post-processing (Model C); stated in §2.2 and revisited as a limitation in §5.3.
  • standard math SUBLINK main-progenitor and main-descendant branches correctly identify the evolutionary track of each subhalo
    Standard merger-tree definition used for all growth-history and descendant analyses (§2.1, §4).
invented entities (1)
  • S6 high-purity and S7 high-completeness NIRCam color–magnitude wedges no independent evidence
    purpose: Provide simulation-optimized selections that recover more M⋆>10^10.5 M⊙ galaxies at z∼5 while excluding stripped low-mass interlopers.
    Defined in §3.3 from exhaustive filter permutations; no independent observational confirmation yet.

reviewed 2026-07-13 · how reviews work

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Cite this review

Pith. "Pith review of What becomes of JWST/NIRCam-selected high-redshift massive galaxies?." pith.science (2026). https://pith.science/paper/LAIRACSP

@misc{pith2026260708820,
  author       = {Pith},
  title        = {Pith review of: What becomes of JWST/NIRCam-selected high-redshift massive galaxies?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LAIRACSP}},
  note         = {Machine review of arXiv:2607.08820}
}
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abstract

Early JWST/NIRCam surveys revealed a puzzling population of high-redshift massive galaxy candidates largely absent from previous rest-frame UV surveys. Spectroscopic follow-up has both confirmed and contested these candidates, whose potential overabundance may challenge traditional models of galaxy formation. In this study we evaluate the reliability of the photometric selections used to identify these candidates in observational data by applying them to galaxies in the TNG300 simulation with synthetic dust-attenuated photometry. Among the five observational selection criteria considered, we find that the selection presented by P\'erez-Gonz\'alez et al. is the most reliable and inclusive. Nevertheless, only 1 of the 18 galaxies at $z\sim5$ with $M_{\star} \geq 10^{11}~\mathrm{M_{\odot}}$ in the simulation satisfies this selection; the remaining 17 galaxies are on average $\sim0.5$ mag bluer than the color selection under the adopted dust model. We construct an improved JWST/NIRCam color-magnitude selection that provides a more complete census of massive galaxies at $z\sim5$ in TNG300 while excluding dusty, low-mass galaxies identified by criteria from the observational literature. We investigate the descendants of the most massive NIRCam-selected galaxies at $z=7,~4,$ and $2$ in TNG300, finding that they rarely evolve into the most massive galaxies by $z=0$. In general only the high-redshift massive galaxies that undergo substantial late-time ($z\lesssim0.2$) merger-driven growth become the most massive galaxies in the Universe today. Together these results suggest that current observational JWST/NIRCam selections are not identifying the most massive high-redshift galaxies, and caution against interpreting high-redshift massive galaxies as direct progenitors of the most massive galaxies at $z=0$.

Figures

Figures reproduced from arXiv: 2607.08820 by Alison L. Coil, Devontae C. Baxter, Ethan O Nadler, Mark Vogelsberger, Xuejian Shen.

Figure 1
Figure 1. Figure 1: Matrix showing the fractional overlap between simulated galaxy populations at z = 5 selected using the five NIRCam pho￾tometric criteria described in [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Panel plot showing color versus magnitude (top left), color versus stellar mass (top right), star formation rate versus stellar mass (bottom left), and stellar mass versus halo mass (bottom right) for simulated galaxies at z = 5. The full galaxy population is represented by black and gray hexagonal bins. The subpopulation of NIRCam-selected galaxies, identified using the criteria of Perez-Gonz ´ alez et al… view at source ↗
Figure 3
Figure 3. Figure 3: JWST/NIRCam color–magnitude diagrams at z = 5 constructed from the F200W, F277W, and F150W filters. The left (right) panel is color-coded by mean stellar mass (purity). We define purity as the fraction of TNG300 galaxies in each color–magnitude bin with M⋆ > 1010.5 M⊙. In this parameter space, the most massive simulated galaxies are efficiently selected with high purity using m150W < 25.3 mag and (m200W − … view at source ↗
Figure 4
Figure 4. Figure 4: Similar to [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of the present-day and descendant stellar and (sub)halo masses (left and right panels, respectively) for the subsample of TNG300 galaxies that are selected at z = 5 by our fiducial criteria and survive to z = 0 without being disrupted. The black and gray hexagonal bins represent the full galaxy population, while the red (blue-hatched) hexagonal bins represent NIRCam-selected galaxies that are ce… view at source ↗
Figure 6
Figure 6. Figure 6: Surface density of dark matter centered on the most massive, median-mass, and least massive galaxies (left, middle, and right columns, respectively) identified using our fiducial NIRCam selection (S1, top two rows) and high-purity selection (S6, bottom two rows). The first and third rows show the large-scale environments of these galaxies at z = 5, while the second and fourth rows show how these environmen… view at source ↗
Figure 7
Figure 7. Figure 7: Stellar mass growth histories for the ten most massive TNG300 galaxies selected at z = 5 based purely on stellar mass (top row), using our fiducial NIRCam color selection (middle row), and using our high-purity NIRCam color selection (bottom row). The solid (dashed) lines show the growth (assembly) histories of galaxies that are among the most massive at z = 5 (z = 0). The colors of the solid and dashed li… view at source ↗
Figure 9
Figure 9. Figure 9: Top row: Stellar mass growth rates for the ten most massive TNG300 galaxies at zselect = 7, 4, 2, and 0. Bottom row: The fraction of the z = 0 stellar mass assembled for the ten most massive galaxies at zselect = 7, 4, 2, and 0. The color bar shows the stellar mass ranking of each galaxy at zselect, and the thick (thin) solid lines highlight galaxies that are (are not) among the ten most massive at z = 0. … view at source ↗

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Works this paper leans on

132 extracted references · 20 canonical work pages · 2 internal anchors

  1. [1]

    B., Casey, C

    Akins, H. B., Casey, C. M., Allen, N., et al. 2023, ApJ, 956, 61, doi: 10.3847/1538-4357/acef21

    doi:10.3847/1538-4357/acef21 2023

  2. [2]

    B., Casey, C

    Akins, H. B., Casey, C. M., Lambrides, E., et al. 2025, ApJ, 991, 37, doi: 10.3847/1538-4357/ade984 Alcalde Pampliega, B., P´erez-Gonz´alez, P. G., Barro, G., et al. 2019, ApJ, 876, 135, doi: 10.3847/1538-4357/ab14f2

    doi:10.3847/1538-4357/ade984 2025

  3. [3]

    L., Teyssier, R., & Dekel, A

    Andalman, Z. L., Teyssier, R., & Dekel, A. 2025, MNRAS, 540, 3350, doi: 10.1093/mnras/staf930 Arrabal Haro, P., Dickinson, M., Finkelstein, S. L., et al. 2023, ApJL, 951, L22, doi: 10.3847/2041-8213/acdd54 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Wh...

    doi:10.1093/mnras/staf930 2025

  4. [4]

    2011, ApJS, 196, 22, doi: 10.1088/0067-0049/196/2/22 16

    Baes, M., Verstappen, J., De Looze, I., et al. 2011, ApJS, 196, 22, doi: 10.1088/0067-0049/196/2/22 16

    doi:10.1088/0067-0049/196/2/22 2011

  5. [5]

    G., Kocevski, D

    Barro, G., P´erez-Gonz´alez, P. G., Kocevski, D. D., et al. 2024, ApJ, 963, 128, doi: 10.3847/1538-4357/ad167e

    doi:10.3847/1538-4357/ad167e 2024

  6. [6]

    A., Weibel, A., et al

    Barrufet, L., Oesch, P. A., Weibel, A., et al. 2023, MNRAS, 522, 449, doi: 10.1093/mnras/stad947

    doi:10.1093/mnras/stad947 2023

  7. [7]

    C., Coil, A

    Baxter, D. C., Coil, A. L., Nadler, E. O., et al. 2025, ApJ, 990, 225, doi: 10.3847/1538-4357/adf644 Ben´ıtez-Llambay, A. 2025, Research Notes of the American Astronomical Society, 9, 122, doi: 10.3847/2515-5172/addab2

    doi:10.3847/1538-4357/adf644 2025

  8. [8]

    Quantifying biases in stellar masses of JWST high-z quasar host galaxies caused by quasar subtraction

    Berger, S., Marshall, M. A., Wyithe, J. S. B., et al. 2025, arXiv e-prints, arXiv:2506.12130, doi: 10.48550/arXiv.2506.12130

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2506.12130 2025

  9. [9]

    2025, MNRAS, 538, 3210, doi: 10.1093/mnras/staf471

    Boylan-Kolchin, M. 2025, MNRAS, 538, 3210, doi: 10.1093/mnras/staf471

    doi:10.1093/mnras/staf471 2025

  10. [10]

    L., & Storchi-Bergmann, T

    Calzetti, D., Kinney, A. L., & Storchi-Bergmann, T. 1994, ApJ, 429, 582, doi: 10.1086/174346

    doi:10.1086/174346 1994

  11. [11]

    2015, Astronomy and Computing, 9, 20, doi: 10.1016/j.ascom.2014.10.004

    Camps, P., & Baes, M. 2015, Astronomy and Computing, 9, 20, doi: 10.1016/j.ascom.2014.10.004

    doi:10.1016/j.ascom.2014.10.004 2015

  12. [12]

    2013, A&A, 560, A35, doi: 10.1051/0004-6361/201322281

    Camps, P., Baes, M., & Saftly, W. 2013, A&A, 560, A35, doi: 10.1051/0004-6361/201322281

    doi:10.1051/0004-6361/201322281 2013

  13. [13]

    C., McLure, R

    Carnall, A. C., McLure, R. J., Dunlop, J. S., et al. 2023a, Nature, 619, 716, doi: 10.1038/s41586-023-06158-6

    doi:10.1038/s41586-023-06158-6

  14. [14]

    C., McLeod, D

    Carnall, A. C., McLeod, D. J., McLure, R. J., et al. 2023b, MNRAS, 520, 3974, doi: 10.1093/mnras/stad369

    doi:10.1093/mnras/stad369

  15. [15]

    2003, PASP, 115, 763, doi: 10.1086/376392

    Chabrier, G. 2003, PASP, 115, 763, doi: 10.1086/376392

    doi:10.1086/376392 2003

  16. [16]

    2026, ApJ, 1001, 131, doi: 10.3847/1538-4357/ae4ede

    Chang, W., Wilson, G., Forrest, B., et al. 2026, ApJ, 1001, 131, doi: 10.3847/1538-4357/ae4ede

    doi:10.3847/1538-4357/ae4ede 2026

  17. [17]

    J., & Wang, K

    Chen, Y ., Mo, H. J., & Wang, K. 2023, MNRAS, 526, 2542, doi: 10.1093/mnras/stad2866

    doi:10.1093/mnras/stad2866 2023

  18. [18]

    M., Chastenet, J., et al

    Chiang, I.-D., Sandstrom, K. M., Chastenet, J., et al. 2018, ApJ, 865, 117, doi: 10.3847/1538-4357/aadc5f

    work page doi:10.3847/1538-4357/aadc5f 2018

  19. [19]

    R., Salim, S., Kereˇs, D., Hayward, C

    Choban, C. R., Salim, S., Kereˇs, D., Hayward, C. C., & Sandstrom, K. M. 2025, MNRAS, 537, 1518, doi: 10.1093/mnras/staf118

    doi:10.1093/mnras/staf118 2025

  20. [20]

    2026, A&A, 707, A29, doi: 10.1051/0004-6361/202557054

    Choe, J., Kimm, T., Katz, H., et al. 2026, A&A, 707, A29, doi: 10.1051/0004-6361/202557054

    work page doi:10.1051/0004-6361/202557054 2026

  21. [21]

    2016, ApJ, 823, 102, doi: 10.3847/0004-637X/823/2/102

    Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102, doi: 10.3847/0004-637X/823/2/102

    doi:10.3847/0004-637x/823/2/102 2016

  22. [22]

    L., Boylan-Kolchin, M., et al

    Chworowsky, K., Finkelstein, S. L., Boylan-Kolchin, M., et al. 2024, AJ, 168, 113, doi: 10.3847/1538-3881/ad57c1

    doi:10.3847/1538-3881/ad57c1 2024

  23. [23]

    Conroy, C., & Gunn, J. E. 2010, ApJ, 712, 833, doi: 10.1088/0004-637X/712/2/833

    doi:10.1088/0004-637x/712/2/833 2010

  24. [24]

    E., & White, M

    Conroy, C., Gunn, J. E., & White, M. 2009, ApJ, 699, 486, doi: 10.1088/0004-637X/699/1/486

    doi:10.1088/0004-637x/699/1/486 2009

  25. [25]

    2023, Nature Astronomy, 7, 622, doi: 10.1038/s41550-023-01918-w

    Curtis-Lake, E., Carniani, S., Cameron, A., et al. 2023, Nature Astronomy, 7, 622, doi: 10.1038/s41550-023-01918-w

    doi:10.1038/s41550-023-01918-w 2023

  26. [26]

    S., & White, S

    Davis, M., Efstathiou, G., Frenk, C. S., & White, S. D. M. 1985, ApJ, 292, 371, doi: 10.1086/163168 de Graaff, A., Setton, D. J., Brammer, G., et al. 2025, Nature Astronomy, 9, 280, doi: 10.1038/s41550-024-02424-3 De Lucia, G., & Blaizot, J. 2007, MNRAS, 375, 2, doi: 10.1111/j.1365-2966.2006.11287.x De Lucia, G., Xie, L., Hirschmann, M., & Fontanot, F. 20...

    doi:10.1086/163168 1985

  27. [27]

    C., Birnboim, Y ., Mandelker, N., & Li, Z

    Dekel, A., Sarkar, K. C., Birnboim, Y ., Mandelker, N., & Li, Z. 2023, MNRAS, 523, 3201, doi: 10.1093/mnras/stad1557

    doi:10.1093/mnras/stad1557 2023

  28. [28]

    2009, MNRAS, 399, 497, doi: 10.1111/j.1365-2966.2009.15034.x

    Dolag, K., Borgani, S., Murante, G., & Springel, V . 2009, MNRAS, 399, 497, doi: 10.1111/j.1365-2966.2009.15034.x

    doi:10.1111/j.1365-2966.2009.15034.x 2009

  29. [29]

    2016, ApJS, 222, 8, doi: 10.3847/0067-0049/222/1/8

    Dotter, A. 2016, ApJS, 222, 8, doi: 10.3847/0067-0049/222/1/8

    doi:10.3847/0067-0049/222/1/8 2016

  30. [30]

    T., & Li, A

    Draine, B. T., & Li, A. 2007, ApJ, 657, 810, doi: 10.1086/511055

    doi:10.1086/511055 2007

  31. [31]

    P., Whitler, L., et al

    Endsley, R., Stark, D. P., Whitler, L., et al. 2023, MNRAS, 524, 2312, doi: 10.1093/mnras/stad1919

    doi:10.1093/mnras/stad1919 2023

  32. [32]

    C., Annunziatella, M., et al

    Forrest, B., Marsan, Z. C., Annunziatella, M., et al. 2020, ApJ, 903, 47, doi: 10.3847/1538-4357/abb819

    doi:10.3847/1538-4357/abb819 2020

  33. [33]

    C., Muzzin, A., et al

    Forrest, B., Cooper, M. C., Muzzin, A., et al. 2024a, ApJ, 977, 51, doi: 10.3847/1538-4357/ad8b1c

    doi:10.3847/1538-4357/ad8b1c

  34. [34]

    C., Shah, E

    Forrest, B., Lemaux, B. C., Shah, E. A., et al. 2024b, ApJ, 971, 169, doi: 10.3847/1538-4357/ad5e78

    doi:10.3847/1538-4357/ad5e78

  35. [35]

    M., Akins, H

    Gentile, F., Casey, C. M., Akins, H. B., et al. 2024, ApJL, 973, L2, doi: 10.3847/2041-8213/ad738a

    doi:10.3847/2041-8213/ad738a 2024

  36. [36]

    C., Forrest, B., et al

    Giddings, F., Lemaux, B. C., Forrest, B., et al. 2026, A&A, 706, A55, doi: 10.1051/0004-6361/202554407 G´omez-Guijarro, C., Magnelli, B., Elbaz, D., et al. 2023, A&A, 677, A34, doi: 10.1051/0004-6361/202346673

    work page doi:10.1051/0004-6361/202554407 2026

  37. [37]

    A., et al

    Gottumukkala, R., Barrufet, L., Oesch, P. A., et al. 2024, MNRAS, 530, 966, doi: 10.1093/mnras/stae754

    doi:10.1093/mnras/stae754 2024

  38. [38]

    E., Labbe, I., Goulding, A

    Greene, J. E., Labbe, I., Goulding, A. D., et al. 2024, ApJ, 964, 39, doi: 10.3847/1538-4357/ad1e5f

    doi:10.3847/1538-4357/ad1e5f 2024

  39. [39]

    A., Sutherland, R

    Groves, B., Dopita, M. A., Sutherland, R. S., et al. 2008, ApJS, 176, 438, doi: 10.1086/528711

    doi:10.1086/528711 2008

  40. [40]

    C., et al

    Gururajan, G., Cucciati, O., Lemaux, B. C., et al. 2025, A&A, 698, A312, doi: 10.1051/0004-6361/202553926

    work page doi:10.1051/0004-6361/202553926 2025

  41. [41]

    E., Wise, J

    Hardin, S. E., Wise, J. H., & Troutman, E. K. 2026, arXiv e-prints, arXiv:2605.30436. https://arxiv.org/abs/2605.30436

    Pith/arXiv arXiv 2026

  42. [42]

    2024, PhRvD, 110, 103540, doi: 10.1103/PhysRevD.110.103540

    Huang, H.-L., Jiang, J.-Q., & Piao, Y .-S. 2024, PhRvD, 110, 103540, doi: 10.1103/PhysRevD.110.103540

    doi:10.1103/physrevd.110.103540 2024

  43. [43]

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

    doi:10.1109/mcse.2007.55 2007

  44. [44]

    2019, ApJ, 878, 68, doi: 10.3847/1538-4357/ab1f0c

    Ito, K., Kashikawa, N., Toshikawa, J., et al. 2019, ApJ, 878, 68, doi: 10.3847/1538-4357/ab1f0c

    doi:10.3847/1538-4357/ab1f0c 2019

  45. [45]

    2021, Research in Astronomy and Astrophysics, 21, 218, doi: 10.1088/1674-4527/21/9/218

    Jing, Y .-J., Rong, Y ., Wang, J., Guo, Q., & Gao, L. 2021, Research in Astronomy and Astrophysics, 21, 218, doi: 10.1088/1674-4527/21/9/218

    work page doi:10.1088/1674-4527/21/9/218 2021

  46. [46]

    2024, A&A, 691, A52, doi: 10.1051/0004-6361/202348857

    Killi, M., Watson, D., Brammer, G., et al. 2024, A&A, 691, A52, doi: 10.1051/0004-6361/202348857

    doi:10.1051/0004-6361/202348857 2024

  47. [47]

    2026, ApJ, 1000, 276, doi: 10.3847/1538-4357/ae4a23

    Kim, H., Kim, J.-H., Jung, M., et al. 2026, ApJ, 1000, 276, doi: 10.3847/1538-4357/ae4a23

    doi:10.3847/1538-4357/ae4a23 2026

  48. [48]

    D., Onoue, M., Inayoshi, K., et al

    Kocevski, D. D., Onoue, M., Inayoshi, K., et al. 2023, ApJL, 954, L4, doi: 10.3847/2041-8213/ace5a0

    doi:10.3847/2041-8213/ace5a0 2023

  49. [49]

    Koyama, Y ., Polletta, M. d. C., Tanaka, I., et al. 2021, MNRAS, 503, L1, doi: 10.1093/mnrasl/slab013 17 Labb´e, I., van Dokkum, P., Nelson, E., et al. 2023, Nature, 616, 266, doi: 10.1038/s41586-023-05786-2

    doi:10.1093/mnrasl/slab013 2021

  50. [50]

    Lagos, C. d. P., Bravo, M., Tobar, R., et al. 2024, MNRAS, 531, 3551, doi: 10.1093/mnras/stae1024

    doi:10.1093/mnras/stae1024 2024

  51. [51]

    2026, ApJL, 1002, L25, doi: 10.3847/2041-8213/ae5824

    Laishram, R., Koyama, Y ., Kusakabe, H., et al. 2026, ApJL, 1002, L25, doi: 10.3847/2041-8213/ae5824

    work page doi:10.3847/2041-8213/ae5824 2026

  52. [52]

    S., et al

    Lambrides, E., Chiaberge, M., Long, A. S., et al. 2024, ApJL, 961, L25, doi: 10.3847/2041-8213/ad11ee

    doi:10.3847/2041-8213/ad11ee 2024

  53. [53]

    2026, arXiv e-prints, arXiv:2601.08693, doi: 10.48550/arXiv.2601.08693

    Lapasia, Y ., Tacchella, S., D’Eugenio, F., et al. 2026, arXiv e-prints, arXiv:2601.08693, doi: 10.48550/arXiv.2601.08693

    work page doi:10.48550/arxiv.2601.08693 2026

  54. [54]

    2019, MNRAS, 490, 1425, doi: 10.1093/mnras/stz2684

    Li, Q., Narayanan, D., & Dav´e, R. 2019, MNRAS, 490, 1425, doi: 10.1093/mnras/stz2684

    doi:10.1093/mnras/stz2684 2019

  55. [55]

    2026, arXiv e-prints, arXiv:2606.19463

    Lim, S. 2026, arXiv e-prints, arXiv:2606.19463. https://arxiv.org/abs/2606.19463

    Pith/arXiv arXiv 2026

  56. [56]

    2024, MNRAS, 532, 4551, doi: 10.1093/mnras/stae1790

    Lim, S., Tacchella, S., Schaye, J., et al. 2024, MNRAS, 532, 4551, doi: 10.1093/mnras/stae1790

    doi:10.1093/mnras/stae1790 2024

  57. [57]

    S., Antwi-Danso, J., Lambrides, E

    Long, A. S., Antwi-Danso, J., Lambrides, E. L., et al. 2024, ApJ, 970, 68, doi: 10.3847/1538-4357/ad4cea

    doi:10.3847/1538-4357/ad4cea 2024

  58. [58]

    J., D’Eugenio, F., Maiolino, R., et al

    Looser, T. J., D’Eugenio, F., Maiolino, R., et al. 2024, Nature, 629, 53, doi: 10.1038/s41586-024-07227-0

    doi:10.1038/s41586-024-07227-0 2024

  59. [59]

    C., Roper, W., Vijayan, A

    Lovell, C. C., Roper, W., Vijayan, A. P., et al. 2023, MNRAS, 525, 5520, doi: 10.1093/mnras/stad2550

    doi:10.1093/mnras/stad2550 2023

  60. [60]

    S., Bose, S., et al

    Lu, S., Frenk, C. S., Bose, S., et al. 2025, MNRAS, 536, 1018, doi: 10.1093/mnras/stae2646

    doi:10.1093/mnras/stae2646 2025

  61. [61]

    1995, ApJ, 441, 18, doi: 10.1086/175332

    Madau, P. 1995, ApJ, 441, 18, doi: 10.1086/175332

    doi:10.1086/175332 1995

  62. [62]

    C., Dickinson, M

    Madau, P., Ferguson, H. C., Dickinson, M. E., et al. 1996, MNRAS, 283, 1388, doi: 10.1093/mnras/283.4.1388

    doi:10.1093/mnras/283.4.1388 1996

  63. [63]

    2018, MNRAS, 480, 5113, doi: 10.1093/mnras/sty2206

    Marinacci, F., V ogelsberger, M., Pakmor, R., et al. 2018, MNRAS, 480, 5113, doi: 10.1093/mnras/sty2206

    doi:10.1093/mnras/sty2206 2018

  64. [64]

    P., Brammer, G., et al

    Matthee, J., Naidu, R. P., Brammer, G., et al. 2024, ApJ, 963, 129, doi: 10.3847/1538-4357/ad2345

    doi:10.3847/1538-4357/ad2345 2024

  65. [65]

    G., Huang, J.-S., & Ashby, M

    Mawatari, K., Yamada, T., Fazio, G. G., Huang, J.-S., & Ashby, M. L. N. 2016, PASJ, 68, 46, doi: 10.1093/pasj/psw041

    work page doi:10.1093/pasj/psw041 2016

  66. [66]

    K., Hashimoto, T., et al

    Mawatari, K., Inoue, A. K., Hashimoto, T., et al. 2020, ApJ, 889, 137, doi: 10.3847/1538-4357/ab6596

    doi:10.3847/1538-4357/ab6596 2020

  67. [67]

    H., & Regan, J

    McCaffrey, J., Hardin, S., Wise, J. H., & Regan, J. A. 2023, The Open Journal of Astrophysics, 6, 47, doi: 10.21105/astro.2304.13755

    doi:10.21105/astro.2304.13755 2023

  68. [68]

    2022, ApJ, 926, 37, doi: 10.3847/1538-4357/ac2b9f

    McConachie, I., Wilson, G., Forrest, B., et al. 2022, ApJ, 926, 37, doi: 10.3847/1538-4357/ac2b9f

    doi:10.3847/1538-4357/ac2b9f 2022

  69. [69]

    2023, MNRAS, 521, 800, doi: 10.1093/mnras/stad586

    Montenegro-Taborda, D., Rodriguez-Gomez, V ., Pillepich, A., et al. 2023, MNRAS, 521, 800, doi: 10.1093/mnras/stad586

    doi:10.1093/mnras/stad586 2023

  70. [70]

    2023, ApJL, 947, L24, doi: 10.3847/2041-8213/acb99e

    Morishita, T., Roberts-Borsani, G., Treu, T., et al. 2023, ApJL, 947, L24, doi: 10.3847/2041-8213/acb99e

    doi:10.3847/2041-8213/acb99e 2023

  71. [71]

    2024, arXiv e-prints, arXiv:2408.10980, doi: 10.48550/arXiv.2408.10980

    Morishita, T., Liu, Z., Stiavelli, M., et al. 2024, arXiv e-prints, arXiv:2408.10980, doi: 10.48550/arXiv.2408.10980

    Pith/arXiv arXiv doi:10.48550/arxiv.2408.10980 2024

  72. [72]

    2026, AJ, 171, 275, doi: 10.3847/1538-3881/ae4e20

    Mould, J. 2026, AJ, 171, 275, doi: 10.3847/1538-3881/ae4e20

    work page doi:10.3847/1538-3881/ae4e20 2026

  73. [73]

    O., Benson, A., Driskell, T., Du, X., & Gluscevic, V

    Nadler, E. O., Benson, A., Driskell, T., Du, X., & Gluscevic, V . 2023, MNRAS, 521, 3201, doi: 10.1093/mnras/stad666

    doi:10.1093/mnras/stad666 2023

  74. [74]

    P., Oesch, P

    Naidu, R. P., Oesch, P. A., Brammer, G., et al. 2026, The Open Journal of Astrophysics, 9, 56033, doi: 10.33232/001c.156033

    doi:10.33232/001c.156033 2026

  75. [75]

    P., Pillepich, A., Springel, V ., et al

    Naiman, J. P., Pillepich, A., Springel, V ., et al. 2018, MNRAS, 477, 1206, doi: 10.1093/mnras/sty618

    doi:10.1093/mnras/sty618 2018

  76. [76]

    2024, Scientific Reports, 14, 3724, doi: 10.1038/s41598-024-52585-4

    Nanayakkara, T., Glazebrook, K., Jacobs, C., et al. 2024, Scientific Reports, 14, 3724, doi: 10.1038/s41598-024-52585-4

    doi:10.1038/s41598-024-52585-4 2024

  77. [77]

    2024, ApJ, 961, 73, doi: 10.3847/1538-4357/ad0966

    Narayanan, D., Lower, S., Torrey, P., et al. 2024, ApJ, 961, 73, doi: 10.3847/1538-4357/ad0966

    doi:10.3847/1538-4357/ad0966 2024

  78. [78]

    I., et al

    Navarro-Carrera, R., Rinaldi, P., Caputi, K. I., et al. 2024, ApJ, 961, 207, doi: 10.3847/1538-4357/ad0df6

    doi:10.3847/1538-4357/ad0df6 2024

  79. [79]

    2014, ApJ, 794, 68, doi: 10.1088/0004-637X/794/1/68

    Nayyeri, H., Mobasher, B., Hemmati, S., et al. 2014, ApJ, 794, 68, doi: 10.1088/0004-637X/794/1/68

    doi:10.1088/0004-637x/794/1/68 2014

  80. [80]

    2024, A&A, 686, A157, doi: 10.1051/0004-6361/202348608

    Nelson, D., Pillepich, A., Ayromlou, M., et al. 2024, A&A, 686, A157, doi: 10.1051/0004-6361/202348608

    doi:10.1051/0004-6361/202348608 2024

Showing first 80 references.

This paper was first reviewed by grok-4.5 on July 13, 2026.