Pith. sign in

REVIEW 2 major objections 5 minor 2 cited by

This paper claims that the scatter of the star-forming main sequence is mostly time-variation within each galaxy, not permanent differences between galaxies, and identifies a two-branch, inward-retreating star-formation cycle as the mechani

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

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

T0 review · deepseek-v4-flash

2026-08-03 19:28 UTC pith:H3A5BUYL

load-bearing objection A careful TNG100 analysis of episodic star formation with a genuinely new two-branch spatial/chemical picture, but the headline scatter accounting is approximate rather than a formal variance decomposition. the 2 major comments →

arxiv 2512.00151 v3 pith:H3A5BUYL submitted 2025-11-28 astro-ph.GA

Episodic Star Formation -- I. Overview and Scatter of the Star-Forming Main Sequence

Yuqian Gui , Dandan Xu , Haoyi Wang , Xuelun Mei , Enci Wang , Cheng Li , Stijn Wuyts This is my paper
classification astro-ph.GA
keywords episodic star formationstar-forming main sequencemain-sequence scattergalaxy formation simulationsgas-phase metallicitystellar metallicitydisk galaxy evolutionstar formation history
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 paper tries to show that the observed scatter of the star-forming main sequence—the roughly 0.25 dex spread in star-formation rates among galaxies of the same stellar mass—is not mainly a permanent difference between galaxies. Following 5,051 simulated present-day star-forming central galaxies backward over 7–8 Gyr, it finds that each galaxy's star-formation rate swings around its own long-term trend by about 0.2 dex, while the historical mean rates of different galaxies differ by about 0.15 dex; these combine to match the observed scatter. The underlying mechanism is an episodic cycle with two branches: an always-on central branch in metal-rich gas, and an outer branch in fresh, metal-poor gas that starts at the outskirts and retreats inward as the episode peaks. If correct, the main sequence is a time-average of asynchronous cycles, and instantaneous star-formation rate is a phase within a cycle rather than a stable galaxy property.

Core claim

The central claim is that episodic star formation—not galaxy-to-galaxy variation—dominates the width of the z=0 star-forming main sequence, and that each episode has a specific spatial-chemical anatomy. Tracing the main progenitor of present-day star-forming disc galaxies back to z≈1, the authors report two star-formation branches per episode: one in heavily metal-enriched gas in galactic centers that can stay active even at global SFR minima, and a second in lower-metallicity gas at galaxy outskirts where fresh gas first arrives, which then retreats inward as the episode builds to peak and subsides. At SFR valleys the young-stellar and star-forming-gas metallicity distributions are bimodal,

What carries the argument

The central object is the two-branch episodic star-formation cycle, with cold gas split into a non-star-forming reservoir and a star-forming pool by a density threshold. The key quantitative identity is the additive decomposition of main-sequence scatter into a temporal fluctuation within each galaxy (σ about 0.2 dex) and a historical offset between galaxies (about 0.15 dex). The cycle is driven by delayed replenishment: the cold non-star-forming reservoir builds up roughly 0.6–0.8 Gyr before SFR rises, then compaction converts it into star-forming gas; feedback eventually depletes the reservoir and tips SFR over. The outward-to-inward retreating branch is the spatial signature that connects

Load-bearing premise

The load-bearing premise is that the simulated episodic cycle—its amplitude, timing, and outward-to-inward spatial pattern—faithfully represents real galaxies rather than being an artifact of the simulation's adopted feedback, cooling, and star-formation recipes.

What would settle it

Run the same peak–valley stacking and scatter decomposition in a cosmological simulation with substantially different subgrid feedback and star-formation prescriptions; if the roughly 0.2 dex intra-galaxy fluctuation and the outward-to-inward retreating branch do not appear, the claim is simulation-specific. Observationally, resolved metallicity maps of young stellar populations in z≈0 star-forming disks that show no bimodality and no phase dependence would contradict the two-branch mechanism.

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

Share X Bluesky LinkedIn Reddit HN

If this is right

  • If the decomposition holds, roughly four-fifths of the z≈0 main-sequence scatter is temporal: a galaxy moves up and down the sequence over roughly 1 Gyr episodes rather than being fixed above or below it.
  • Galaxies currently above the ridge have tended to live above it since z≈1, while the amplitude of a galaxy's fluctuation is not correlated with its historical offset.
  • Because the outer branch fluctuates more than the central one, measured scatter depends on the aperture or radius used, with outskirts showing substantially larger temporal variation.
  • Young-star disc sizes oscillate with the cycle—larger at valleys, smaller at peaks—so UV-selected galaxy sizes should inherit extra scatter from the phase of the star-formation episode.
  • Metallicity distributions of young stars and star-forming gas distinguish peaks from valleys, offering a phase indicator that could locate a galaxy within its episode observationally.

Where Pith is reading between the lines

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

  • If the cycle is universal, the star-forming main sequence is an ensemble average of asynchronous cycles, not a locus of equilibrium states; single-epoch SFR tracers then measure a phase, and gas fractions or depletion times should show cyclic covariances on Gyr timescales.
  • The inward-retreating outer branch predicts radial stellar population gradients within individual disks: stars born early in each episode should be more metal-poor and at larger radii than stars born later, which resolved young-stellar-population maps of nearby face-on disks could test.
  • The same cycle may explain why SFR indicators on different timescales (such as H-alpha versus UV versus infrared) disagree by order 0.2 dex; comparing short- and long-timescale indicators across many galaxies would provide an observational check of the scatter decomposition.

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. This paper uses the TNG100 cosmological simulation to trace the main progenitor branches of z=0 star-forming central galaxies (M* ≥ 10^9.5 Msun) back to z≈1, and argues that the scatter of the star-forming main sequence is driven by episodic star formation. It identifies a two-branch pattern in each star-forming episode: a central, metal-enriched star-forming branch that is often continuously active, and an outer, lower-metallicity branch that starts at the galaxy outskirts and progressively retreats inward as the episode evolves. The paper further shows that young-star and cold-gas metallicity distributions differ between SFR peaks and valleys, with bimodal metallicities at valleys. The central quantitative claim is that the temporal SFR fluctuation within individual galaxies (σ_SFR,rel ~ 0.20 dex) together with the galaxy-to-galaxy differentiation in historical mean MS offset (avg(ΔMS) ~ 0.15 dex) can account for the z=0 SFMS scatter (~0.25 dex). Quantitative peak-valley stacking is based on a refined subsample of 191 of the 285 star-forming rotating disc galaxies, selected to have peak-valley SFR contrasts > 0.5 dex.

Significance. If the two-branch, retreating star-formation picture is correct, it provides a concrete physical mechanism for episodic star formation and yields testable predictions: bimodal metallicity PDFs at SFR minima, phase-dependent size variations of young stellar populations, and a dominant temporal contribution to the SFMS scatter. The paper builds on a public, widely used simulation, presents a clear example galaxy, documents the pair-search algorithm, and includes threshold sensitivity tests in Appendix B. It also explicitly acknowledges that the conclusions are tied to TNG100's subgrid feedback model. However, the headline quantitative claim about the scatter decomposition is not currently demonstrated; it requires a formal variance decomposition rather than a comparison of percentile widths.

major comments (2)
  1. [Section 2.2 and Section 4, first paragraph] The central claim that ~0.2 dex temporal and ~0.15 dex intrinsic fluctuations 'well account' for the ~0.25 dex SFMS scatter is not established. The paper only compares 16–84 percentile widths of ΔMS_z=0, avg(ΔMS), and σ_SFR,rel; percentile widths do not add in quadrature in general, and no formal variance decomposition is presented. To support the claim, the authors need to verify an identity such as Var(ΔMS_z=0) ≈ Var(avg(ΔMS)) + E[σ_SFR,rel²] + 2Cov(avg(ΔMS), σ_SFR,rel), with all terms defined on the same galaxies and time window. This is not guaranteed by construction: σ_SFR,rel is the RMS residual around a per-galaxy linear fit to log SFR–log(1+z), not around the historical mean MS offset, and ΔMS_z=0 is one time point, so its variance involves the autocorrelation function. The quadrature agreement may be coincidental.
  2. [Section 2.3, 3.3, and Appendix B] The two-branch star-formation pattern is presented as typical/universal, but all quantitative stacked peak–valley results use the 191/285 galaxies selected with a >0.5 dex peak-to-valley threshold, i.e., episodes twice as large as the typical 0.25 dex scatter. The universality claim rests on 'visual inspection' of the remaining galaxies (last sentence of Appendix B), which is not a reproducible metric. The Appendix B threshold tests show how pair counts and timescales depend on the threshold, but they do not test whether the two-branch morphology and the metallicity bimodality survive at lower thresholds. Either a quantitative pattern-recognition test should be applied to all 285 galaxies, or the text should explicitly restrict the morphological claims to the high-amplitude sample.
minor comments (5)
  1. [Section 2.2] Please clarify whether the MS ridge at z<1 is fit using only star-forming progenitors or all main-branch galaxies, and specify the fitting method (e.g., treatment of outliers and quenched objects).
  2. [Section 5, conclusions bullet] In the bullet list, 'the 1σ of temporal fluctuation within each galaxy is ΔMS is 0.200...' should read 'σ_SFR,rel is 0.200...'.
  3. [Section 4 and Fig. 9] The statement that the historical main-sequence offset among all progenitors since z~1 is 0.006+0.245−0.272 dex pools snapshots across time and redshift; this is not the same statistic as ΔMS_z=0 and should be labelled as a snapshot-pooled scatter to avoid confusion.
  4. [Fig. 5 caption and Section 3.3] The plotted quantity is a difference between snapshot i+1 and i−1, not a derivative; the caption should state that it measures a change over ~0.4 Gyr.
  5. [Appendix A] The stop ratio 0.7 in the pair-search algorithm is a tuning parameter; the statement that values >0.5 do not change results would be more convincing if supported by a brief test.

Circularity Check

0 steps flagged

No significant circularity: the scatter decomposition is descriptive and not fitted to its target; self-citations are motivational, not load-bearing.

full rationale

The paper's central quantitative claim is that the temporal SFR fluctuation within galaxies (~0.2 dex) plus the galaxy-to-galaxy differentiation in historical mean offset (~0.15 dex) can account for the z=0 SFMS scatter (~0.25 dex). These numbers are not predictions obtained by fitting a parameter to the 0.25 dex scatter; they are independently measured summary statistics of the same TNG100 SFR histories. The approximate quadrature agreement is nontrivial: sigma_SFR,rel is defined as the RMS scatter around a per-galaxy linear fit to log SFR versus log(1+z), not around avg(DeltaMS), and no equation in the paper forces sqrt(0.15^2 + 0.20^2) = 0.25. If the measured components had been inconsistent with the total width, the claim would have failed. The lack of a formal variance decomposition (e.g., Var(DeltaMS_z=0) vs Var(avg(DeltaMS)) + E[sigma_SFR,rel^2]) is a support gap or presentation weakness, but it is not circularity: the conclusion is not equivalent to its inputs by construction. The self-citations (Wang et al. 2022; Lu et al. 2021, 2022) are used for motivation and sample construction, while the physical findings — two star-formation branches, metallicity bimodality at peaks/valleys, and the scatter decomposition — are derived from TNG100 data and are externally checkable. No uniqueness theorem or ansatz is imported from the authors' prior work as a load-bearing premise. The paper explicitly states in Section 5 that the conclusions are entirely based on TNG100 and that details are subject to the adopted feedback models, an honest limitation rather than a circular step. Overall, I find no step where a prediction reduces by definition to a fitted input or to a self-citation.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 0 invented entities

No new physical entities are introduced. The 'two branches' are descriptive labels for star-formation locations, not new forces or particles. The SF and non-SF cold gas components are operational splits of the simulated gas, and the free parameters are analysis thresholds and cuts that affect the quantitative results.

free parameters (4)
  • Peak-valley amplitude threshold = 0.5 dex
    Used in Section 2.3 to select 191 of 285 disk galaxies for peak-valley stacking; directly affects the number of episodes, the peak-valley timescale (~1.25 Gyr), and the metallicity/size distributions.
  • Pair-search stop ratio = 0.7
    Appendix A: a peak-valley pair is accepted when the remaining drop is >0.7 of the peak-valley rise. The authors note that values >0.5 do not change results qualitatively, but the choice still shapes which pairs are counted.
  • sSFR star-forming cut = log sSFR >= -1.5 Gyr^-1
    Defines the SFMS sample in Section 2.2, yielding 5051 galaxies; the cut is taken from prior literature and affects the measured scatter and the sample composition.
  • Young-star age threshold = 100 Myr
    Defines 'young stars' in Figures 5-7 for size and metallicity statistics; changing this threshold would change the inferred radial and metallicity distributions at peaks and valleys.
axioms (4)
  • domain assumption TNG100 with its subgrid feedback, star-formation, and chemical enrichment prescriptions adequately models the episodic star-formation behavior of real galaxies.
    All results are computed from TNG100 (Section 2.1), and Section 5 explicitly acknowledges the conclusions are entirely based on this simulation and subject to its adopted feedback models.
  • domain assumption The main progenitor branch identified by SUBFIND merger trees from z=1 to z=0 represents each galaxy's true evolutionary path.
    Section 2.2 relies on tracing the main progenitor branch; if merger-tree branch switching or misidentification occurs, the reconstructed SFR histories and peak-valley pairs would be affected.
  • domain assumption A linear relation in log SFR versus log(1+z) is an adequate long-term baseline for each galaxy, so residuals measure temporal fluctuation rather than secular evolution.
    Section 2.2 defines sigma_SFR,rel by fitting this line; curvature, breaks, or mass-dependent evolution in the baseline would change the residual scatter.
  • domain assumption The observed SFMS scatter at z=0 is approximately 0.25 dex, as cited from the literature.
    The comparison target in Section 4 is taken from observational surveys (Speagle et al. 2014, Pearson et al. 2018, etc.) and is not re-measured or scrutinized in this work.

pith-pipeline@v1.3.0-alltime-deepseek · 26585 in / 12319 out tokens · 120867 ms · 2026-08-03T19:28:53.149161+00:00 · methodology

0 comments
Cite this review

Pith. "Pith review of Episodic Star Formation -- I. Overview and Scatter of the Star-Forming Main Sequence." pith.science (2026). https://pith.science/paper/H3A5BUYL

@misc{pith2026251200151,
  author       = {Pith},
  title        = {Pith review of: Episodic Star Formation -- I. Overview and Scatter of the Star-Forming Main Sequence},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H3A5BUYL}},
  note         = {Machine review of arXiv:2512.00151}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Episodic star formation cycles in both high- and low-redshift galaxies have gained more and more evidence. This paper aims to understand the detailed physical processes behind such behaviors and investigate how such an episodic star-forming scenario can explain the scatter in star-formation rate (SFR) of star-forming main-sequence galaxies. This is achieved through tracing back in time the history of z=0 star-forming central galaxies in the TNG100 simulation over the past 7-8 Gyrs. As the first paper in this series, we provide an overview of the episodic star formation history. We find that two branches of star formation typically develop during each episode: while one branch happens in heavily metal-enriched gas in the centers of galaxies, a secondary branch starts in lower-metallicity regions at galaxy outskirts where fresh gas first arrives, and gradually progresses to inner regions of galaxies. Additionally, the temporal variation in the SFR at galaxy outskirts is more significant than that at centers. As a consequence, the metallicities in both gas and young stars exhibit remarkably different distributions between SFR peaks and valleys. The resulting temporal SFR fluctuation within individual galaxies has an average of ~ 0.2 dex, while the intrinsic differentiation between (the historical mean of) galaxies is ~ 0.15 dex. These two together can well account for the scatter in SFR of ~ 0.25 dex as observed for z=0 star-forming main-sequence galaxies.

Figures

Figures reproduced from arXiv: 2512.00151 by Cheng Li, Dandan Xu, Enci Wang, Haoyi Wang, Stijn Wuyts, Xuelun Mei, Yuqian Gui.

Figure 1
Figure 1. Figure 1: The star formation history of one of our sample galaxies (ID at z = 0 is 504650). The red line represents the SFR of this galaxy evaluated within 2Rhsm. The green and blue upper triangles represent the valleys and peaks in the upward pairs. The blue and green hollow hexagons represent peaks and valleys in the downward pairs. valley pair. For the SFH of each galaxy, we search for valley-to-peak (upward) pai… view at source ↗
Figure 2
Figure 2. Figure 2: The g − r color - log M∗ evolutionary track for one example galaxy (ID-433317). The contours indicate the distribution of color as a function of stellar mass M∗ of cen￾tral galaxies in today’s universe (red contour) and at z = 0.7 (green contour), respectively. The black solid line marks the evolutionary track of the galaxy, with circles color-coded by redshifts. galaxy-to-galaxy difference among the popul… view at source ↗
Figure 3
Figure 3. Figure 3: The episodic star-formation history and prop￾erty evolution for one sample galaxy (ID-601819). The top panel presents the temporal variation of SFR⩽2Rhsm (global, in solid line) and SFR⩽0.5Rhsm (central, in dashed line). The second panel presents the evolution of cold gas mass within 2Rhsm for the star-forming gas (MSF, solid line) and the cold non-SF gas (Mcold, non−SF, dashed line). The third panel shows… view at source ↗
Figure 4
Figure 4. Figure 4: The relative mass ∆ log M within 2Rhsm of both star-forming gas (blue) and cold non-SF gas (magenta) as a function of time offset around the nearest peak/valley moment. In both panels, the shaded regions represent the central 68th percentile distribution. For clarity of visualization, the red dashed line marks the median SFR within 2Rhsm. We will investigate how such an episodic star-formation behavior can… view at source ↗
Figure 5
Figure 5. Figure 5: The histograms of the increment in normalized half-stellar-mass radius of young stars Rhsm, <100 Myr/Rhsm from the (i − 1)th snapshot to the (i + 1)th snapshot, where i is a snapshot at either SFR valley or peak. The blue and red histograms represent the increment in normalized size around peaks and valleys, respectively. The dashed lines and shaded regions represent the corresponding median and central 68… view at source ↗
Figure 6
Figure 6. Figure 6: Left Panel: The mass-weighted metallicity distribution of young stars, log Z∗, <100Myr, stacked at SFR peaks (blue) and valleys (red). The error bars represent the standard errors of the mean. The darker and lighter shaded regions represent the distributions of averaged young stellar metallicity calculated within and outside 2Rhsm. Right Panel: The mass-weighted distribution of normalized galactocentric ra… view at source ↗
Figure 7
Figure 7. Figure 7 [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: (a): The stacked histogram of average mass-weighted total cold gas metallicity, log Zcold, at SFR peaks (blue) and valleys (red) within 10Rhsm. The darker and lighter shaded regions represent the distributions calculated for star-forming and cold non-SF gas, respectively. Plotting style similar to [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The correlation between the main-sequence offset at z = 0 (∆MSz=0), the historically averaged main-sequence offset since z = 1 (avg(∆MS)) and the temporal fluctua￾tion of individual galaxies since z = 1 (σSFR,rel). The SFR is measured within 2Rhsm. The 16-50-84 percentile of each quantity is shown above the corresponding panel. Therefore, we expect that the episodic star-formation behavior causing temporal… view at source ↗
Figure 10
Figure 10. Figure 10: Left Panel: Similar to [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Left Panel: The number of pairs per galaxy as a function of ∆ log [SFR/M⊙Gyr−1 ] threshold. Blue triangles represent the upward pairs, and orange hexagons represent the downward pairs. At each threshold, the number of galaxies that possess such episodic star-forming features is also labeled beside the symbol. Right Panel: The median and central 68th percentiles of time intervals of the pairs in different … view at source ↗

discussion (0)

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

Forward citations

Cited by 2 Pith papers

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

  1. Do we understand the star formation history of the universe?

    astro-ph.GA 2026-07 conditional novelty 6.0

    Star formation rates required by the evolving stellar mass function yield a main sequence that agrees with JWST spectroscopy and theory but differs from prior concordance relations.

  2. The Galaxy Stellar Mass-SFR-Size Relation in EAGLE, TNG100, and Observations

    astro-ph.GA 2026-03 conditional novelty 6.0

    EAGLE and TNG100 both reproduce the observed trend that galaxies at fixed stellar mass are more compact when their star formation deviates from the main sequence, though EAGLE predicts a stronger and TNG100 a weaker d...

Reference graph

Works this paper leans on

130 extracted references · 8 canonical work pages · cited by 2 Pith papers

  1. [1]

    2017, MNRAS, 472, L109, doi: 10.1093/mnrasl/slx161 Astropy Collaboration, Robitaille, T

    Angl´ es-Alc´ azar, D., Faucher-Gigu` ere, C.-A., Quataert, E., et al. 2017, MNRAS, 472, L109, doi: 10.1093/mnrasl/slx161 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc...

    doi:10.1093/mnrasl/slx161 2017

  2. [2]

    K., Glazebrook, K., Brinkmann, J., et al

    Baldry, I. K., Glazebrook, K., Brinkmann, J., et al. 2004, ApJ, 600, 681, doi: 10.1086/380092

    doi:10.1086/380092 2004

  3. [3]

    L., Baldry, I

    Balogh, M. L., Baldry, I. K., Nichol, R., et al. 2004, ApJL, 615, L101, doi: 10.1086/426079

    doi:10.1086/426079 2004

  4. [4]

    E., & Hernquist, L

    Barnes, J. E., & Hernquist, L. E. 1991, ApJL, 370, L65, doi: 10.1086/185978

    doi:10.1086/185978 1991

  5. [5]

    H., Hearin, A

    Behroozi, P., Wechsler, R. H., Hearin, A. P., & Conroy, C. 2019, MNRAS, 488, 3143, doi: 10.1093/mnras/stz1182

    doi:10.1093/mnras/stz1182 2019

  6. [6]

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

    Bell, E. F., Wolf, C., Meisenheimer, K., et al. 2004, ApJ, 608, 752, doi: 10.1086/420778

    doi:10.1086/420778 2004

  7. [7]

    E., Macci` o, A

    Blank, M., Meier, L. E., Macci` o, A. V., et al. 2021, MNRAS, 500, 1414, doi: 10.1093/mnras/staa2670

    doi:10.1093/mnras/staa2670 2021

  8. [8]

    Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151, doi: 10.1111/j.1365-2966.2004.07881.x Budav´ ari, T., Connolly, A. J., Szalay, A. S., et al. 2003, ApJ, 595, 59, doi: 10.1086/377168

    arXiv 2004

  9. [9]

    2019, MNRAS, 487, 3845, doi: 10.1093/mnras/stz1449

    Caplar, N., & Tacchella, S. 2019, MNRAS, 487, 3845, doi: 10.1093/mnras/stz1449

    doi:10.1093/mnras/stz1449 2019

  10. [10]

    M., Binney, J., et al

    Cattaneo, A., Faber, S. M., Binney, J., et al. 2009, Nature, 460, 213, doi: 10.1038/nature08135

    doi:10.1038/nature08135 2009

  11. [11]

    2024, MNRAS, 527, 7871, doi: 10.1093/mnras/stad3709

    Cenci, E., Feldmann, R., Gensior, J., et al. 2024, MNRAS, 527, 7871, doi: 10.1093/mnras/stad3709

    doi:10.1093/mnras/stad3709 2024

  12. [12]

    2009, ApJ, 695, 292, doi: 10.1088/0004-637X/695/1/292

    Ceverino, D., & Klypin, A. 2009, ApJ, 695, 292, doi: 10.1088/0004-637X/695/1/292

    doi:10.1088/0004-637x/695/1/292 2009

  13. [13]

    2016, A&A, 588, A41, doi: 10.1051/0004-6361/201424514

    Cicone, C., Maiolino, R., & Marconi, A. 2016, A&A, 588, A41, doi: 10.1051/0004-6361/201424514

    doi:10.1051/0004-6361/201424514 2016

  14. [14]

    2014, A&A, 562, A21, doi: 10.1051/0004-6361/201322464

    Cicone, C., Maiolino, R., Sturm, E., et al. 2014, A&A, 562, A21, doi: 10.1051/0004-6361/201322464

    doi:10.1051/0004-6361/201322464 2014

  15. [15]

    2024, A&A, 686, A128, doi: 10.1051/0004-6361/202348091

    Ciesla, L., Elbaz, D., Ilbert, O., et al. 2024, A&A, 686, A128, doi: 10.1051/0004-6361/202348091

    doi:10.1051/0004-6361/202348091 2024

  16. [16]

    Ciotti, L., & Ostriker, J. P. 2007, ApJ, 665, 1038, doi: 10.1086/519833

    doi:10.1086/519833 2007

  17. [17]

    2003, AJ, 126, 1183, doi: 10.1086/377318

    Papovich, C. 2003, AJ, 126, 1183, doi: 10.1086/377318

    doi:10.1086/377318 2003

  18. [20]

    2007, ApJ, 670, 156, doi: 10.1086/521818 De Lucia, G., Xie, L., Fontanot, F., & Hirschmann, M

    Daddi, E., Dickinson, M., Morrison, G., et al. 2007, ApJ, 670, 156, doi: 10.1086/521818 De Lucia, G., Xie, L., Fontanot, F., & Hirschmann, M. 2020, MNRAS, 498, 3215, doi: 10.1093/mnras/staa2556

    doi:10.1086/521818 2007

  19. [21]

    2006, MNRAS, 368, 2, doi: 10.1111/j.1365-2966.2006.10145.x

    Dekel, A., & Birnboim, Y. 2006, MNRAS, 368, 2, doi: 10.1111/j.1365-2966.2006.10145.x

    arXiv 2006

  20. [22]

    2014, MNRAS, 438, 1870, doi: 10.1093/mnras/stt2331

    Dekel, A., & Burkert, A. 2014, MNRAS, 438, 1870, doi: 10.1093/mnras/stt2331

    doi:10.1093/mnras/stt2331 2014

  21. [23]

    1986, ApJ, 303, 39, doi: 10.1086/164050

    Dekel, A., & Silk, J. 1986, ApJ, 303, 39, doi: 10.1086/164050

    doi:10.1086/164050 1986

  22. [24]

    2013, MNRAS, 435, 999, doi: 10.1093/mnras/stt1338

    Dekel, A., Zolotov, A., Tweed, D., et al. 2013, MNRAS, 435, 999, doi: 10.1093/mnras/stt1338

    doi:10.1093/mnras/stt1338 2013

  23. [25]

    2009, Nature, 457, 451, doi: 10.1038/nature07648 Di Matteo, T., Springel, V., & Hernquist, L

    Dekel, A., Birnboim, Y., Engel, G., et al. 2009, Nature, 457, 451, doi: 10.1038/nature07648 Di Matteo, T., Springel, V., & Hernquist, L. 2005, Nature, 433, 604, doi: 10.1038/nature03335

    doi:10.1038/nature07648 2009

  24. [27]

    2025, MNRAS, 537, 629, doi: 10.1093/mnras/staf006

    Sijacki, D. 2025, MNRAS, 537, 629, doi: 10.1093/mnras/staf006

    doi:10.1093/mnras/staf006 2025

  25. [28]

    T., McLeod, D

    Donnan, C. T., McLeod, D. J., Dunlop, J. S., et al. 2023, MNRAS, 518, 6011, doi: 10.1093/mnras/stac3472

    doi:10.1093/mnras/stac3472 2023

  26. [29]

    T., McLure, R

    Donnan, C. T., McLure, R. J., Dunlop, J. S., et al. 2024, MNRAS, 533, 3222, doi: 10.1093/mnras/stae2037

    doi:10.1093/mnras/stae2037 2024

  27. [30]

    Draine, B. T. 2011, Physics of the Interstellar and Intergalactic Medium

    2011

  28. [32]

    2016, ApJ, 820, 131, doi: 10.3847/0004-637X/820/2/131

    El-Badry, K., Wetzel, A., Geha, M., et al. 2016, ApJ, 820, 131, doi: 10.3847/0004-637X/820/2/131

    doi:10.3847/0004-637x/820/2/131 2016

  29. [33]

    2007, A&A, 468, 33, doi: 10.1051/0004-6361:20077525

    Elbaz, D., Daddi, E., Le Borgne, D., et al. 2007, A&A, 468, 33, doi: 10.1051/0004-6361:20077525

    doi:10.1051/0004-6361:20077525 2007

  30. [34]

    2024a, arXiv e-prints, arXiv:2410.01905, doi: 10.48550/arXiv.2410.01905 17

    Whitler, L. 2024a, arXiv e-prints, arXiv:2410.01905, doi: 10.48550/arXiv.2410.01905 17

    Pith/arXiv arXiv doi:10.48550/arxiv.2410.01905

  31. [35]

    P., Whitler, L., et al

    Endsley, R., Stark, D. P., Whitler, L., et al. 2024b, MNRAS, 533, 1111, doi: 10.1093/mnras/stae1857

    doi:10.1093/mnras/stae1857

  32. [36]

    M., Willmer, C

    Faber, S. M., Willmer, C. N. A., Wolf, C., et al. 2007, ApJ, 665, 265, doi: 10.1086/519294

    doi:10.1086/519294 2007

  33. [37]

    Fabian, A. C. 2012, ARA&A, 50, 455, doi: 10.1146/annurev-astro-081811-125521

    doi:10.1146/annurev-astro-081811-125521 2012

  34. [38]

    A., Katz, N., Gardner, J

    Fardal, M. A., Katz, N., Gardner, J. P., et al. 2001, ApJ, 562, 605, doi: 10.1086/323519 Faucher-Gigu` ere, C.-A. 2018, MNRAS, 473, 3717, doi: 10.1093/mnras/stx2595

    work page doi:10.1086/323519 2001

  35. [39]

    F., Faucher-Gigu` ere, C.-A., & Kereˇ s, D

    Feldmann, R., Quataert, E., Hopkins, P. F., Faucher-Gigu` ere, C.-A., & Kereˇ s, D. 2017, MNRAS, 470, 1050, doi: 10.1093/mnras/stx1120

    doi:10.1093/mnras/stx1120 2017

  36. [40]

    2023, MNRAS, 522, 3831, doi: 10.1093/mnras/stad1205

    Feldmann, R., Quataert, E., Faucher-Gigu` ere, C.-A., et al. 2023, MNRAS, 522, 3831, doi: 10.1093/mnras/stad1205

    doi:10.1093/mnras/stad1205 2023

  37. [41]

    C., Krumholz, M

    Forbes, J. C., Krumholz, M. R., Burkert, A., & Dekel, A. 2014, MNRAS, 438, 1552, doi: 10.1093/mnras/stt2294 Fortun´ e, Silvio amnd Remus, R.-S., Kimmig, L. C.,

    doi:10.1093/mnras/stt2294 2014

  38. [42]

    2025, arXiv e-prints, arXiv:2503.20858, doi: 10.48550/arXiv.2503.20858

    Burkert, A., & Dolag, K. 2025, arXiv e-prints, arXiv:2503.20858, doi: 10.48550/arXiv.2503.20858

    doi:10.48550/arxiv.2503.20858 2025

  39. [43]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

    doi:10.1038/s41586-020-2649-2 2020

  40. [44]

    C., & Hopkins, P

    Hayward, C. C., & Hopkins, P. F. 2017, MNRAS, 465, 1682, doi: 10.1093/mnras/stw2888

    doi:10.1093/mnras/stw2888 2017

  41. [45]

    2014, MNRAS, 442, 2304, doi: 10.1093/mnras/stu1023

    Hirschmann, M., Dolag, K., Saro, A., et al. 2014, MNRAS, 442, 2304, doi: 10.1093/mnras/stu1023

    doi:10.1093/mnras/stu1023 2014

  42. [46]

    W., Blanton, M

    Hogg, D. W., Blanton, M. R., Eisenstein, D. J., et al. 2003, ApJL, 585, L5, doi: 10.1086/374238

    work page doi:10.1086/374238 2003

  43. [47]

    F., Kereˇ s, D., O˜ norbe, J., et al

    Hopkins, P. F., Kereˇ s, D., O˜ norbe, J., et al. 2014a, MNRAS, 445, 581, doi: 10.1093/mnras/stu1738 —. 2014b, MNRAS, 445, 581, doi: 10.1093/mnras/stu1738

    doi:10.1093/mnras/stu1738

  44. [48]

    F., Somerville, R

    Hopkins, P. F., Somerville, R. S., Hernquist, L., et al. 2006, ApJ, 652, 864, doi: 10.1086/508503

    doi:10.1086/508503 2006

  45. [49]

    F., Wetzel, A., Kereˇ s, D., et al

    Hopkins, P. F., Wetzel, A., Kereˇ s, D., et al. 2018, MNRAS, 480, 800, doi: 10.1093/mnras/sty1690

    doi:10.1093/mnras/sty1690 2018

  46. [50]

    F., Wetzel, A., Wheeler, C., et al

    Hopkins, P. F., Wetzel, A., Wheeler, C., et al. 2023, MNRAS, 519, 3154, doi: 10.1093/mnras/stac3489

    doi:10.1093/mnras/stac3489 2023

  47. [51]

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

    doi:10.1109/mcse.2007.55 2007

  48. [52]

    G., Tacchella, S., Genel, S., et al

    Iyer, K. G., Tacchella, S., Genel, S., et al. 2020, MNRAS, 498, 430, doi: 10.1093/mnras/staa2150

    doi:10.1093/mnras/staa2150 2020

  49. [53]

    Katz, N., & Gunn, J. E. 1991, ApJ, 377, 365, doi: 10.1086/170367

    doi:10.1086/170367 1991

  50. [55]

    2024, arXiv e-prints, arXiv:2405.04578, doi: 10.48550/arXiv.2405.04578

    Kravtsov, A., & Belokurov, V. 2024, arXiv e-prints, arXiv:2405.04578, doi: 10.48550/arXiv.2405.04578

    Pith/arXiv arXiv doi:10.48550/arxiv.2405.04578 2024

  51. [56]

    2023, ApJL, 942, L26, doi: 10.3847/2041-8213/ac959b

    Leethochawalit, N., Trenti, M., Santini, P., et al. 2023, ApJL, 942, L26, doi: 10.3847/2041-8213/ac959b

    work page doi:10.3847/2041-8213/ac959b 2023

  52. [57]

    K., Schinnerer, E., Liu, D., et al

    Leslie, S. K., Schinnerer, E., Liu, D., et al. 2020, ApJ, 899, 58, doi: 10.3847/1538-4357/aba044

    doi:10.3847/1538-4357/aba044 2020

  53. [58]

    J., Carollo, C

    Lilly, S. J., Carollo, C. M., Pipino, A., Renzini, A., & Peng, Y. 2013, ApJ, 772, 119, doi: 10.1088/0004-637X/772/2/119

    doi:10.1088/0004-637x/772/2/119 2013

  54. [59]

    2022, MNRAS, 509, 5062, doi: 10.1093/mnras/stab3228

    Lu, S., Xu, D., Wang, S., et al. 2022, MNRAS, 509, 5062, doi: 10.1093/mnras/stab3228

    work page doi:10.1093/mnras/stab3228 2022

  55. [60]

    2021, MNRAS, 503, 726, doi: 10.1093/mnras/stab497

    Lu, S., Xu, D., Wang, Y., et al. 2021, MNRAS, 503, 726, doi: 10.1093/mnras/stab497

    work page doi:10.1093/mnras/stab497 2021

  56. [61]

    2025, ApJL, 981, L6, doi: 10.3847/2041-8213/adb4ed

    Lyu, C., Wang, E., Zhang, H., et al. 2025, ApJL, 981, L6, doi: 10.3847/2041-8213/adb4ed

    doi:10.3847/2041-8213/adb4ed 2025

  57. [62]

    2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615

    Madau, P., & Dickinson, M. 2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615

    doi:10.1146/annurev-astro-081811-125615 2014

  58. [63]

    2010, MNRAS, 408, 2115, doi: 10.1111/j.1365-2966.2010.17291.x

    Gnerucci, A. 2010, MNRAS, 408, 2115, doi: 10.1111/j.1365-2966.2010.17291.x

    arXiv 2010

  59. [64]

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

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

    doi:10.1093/mnras/sty2206 2018

  60. [65]

    C., et al

    Martizzi, D., Vogelsberger, M., Artale, M. C., et al. 2019, MNRAS, 486, 3766, doi: 10.1093/mnras/stz1106

    doi:10.1093/mnras/stz1106 2019

  61. [66]

    P., Patel, M

    Mason, J. P., Patel, M. R., Pajola, M., et al. 2023, Journal of Geophysical Research (Planets), 128, e2023JE008002, doi: 10.1029/2023JE008002

    work page doi:10.1029/2023je008002 2023

  62. [67]

    2019, MNRAS, 484, 915, doi: 10.1093/mnras/stz030

    Matthee, J., & Schaye, J. 2019, MNRAS, 484, 915, doi: 10.1093/mnras/stz030

    doi:10.1093/mnras/stz030 2019

  63. [68]

    2025, arXiv e-prints, arXiv:2503.00106, doi: 10.48550/arXiv.2503.00106

    McClymont, W., Tacchella, S., Smith, A., et al. 2025, arXiv e-prints, arXiv:2503.00106, doi: 10.48550/arXiv.2503.00106

    doi:10.48550/arxiv.2503.00106 2025

  64. [69]

    McQuinn, K. B. W., Skillman, E. D., Cannon, J. M., et al. 2010a, ApJ, 721, 297, doi: 10.1088/0004-637X/721/1/297 —. 2010b, ApJ, 724, 49, doi: 10.1088/0004-637X/724/1/49

    doi:10.1088/0004-637x/721/1/297

  65. [70]

    J., Coil, A

    Mendez, A. J., Coil, A. L., Lotz, J., et al. 2011, ApJ, 736, 110, doi: 10.1088/0004-637X/736/2/110

    doi:10.1088/0004-637x/736/2/110 2011

  66. [71]

    C., & Hernquist, L

    Mihos, J. C., & Hernquist, L. 1996, ApJ, 464, 641, doi: 10.1086/177353

    doi:10.1086/177353 1996

  67. [72]

    J., Greene, J

    Mintz, A., Setton, D. J., Greene, J. E., et al. 2025, arXiv e-prints, arXiv:2506.16510, doi: 10.48550/arXiv.2506.16510

    doi:10.48550/arxiv.2506.16510 2025

  68. [73]

    2017, A&A, 597, A97, doi: 10.1051/0004-6361/201629409 Mu˜ noz L´ opez, C., Krajnovi´ c, D., Epinat, B., et al

    Morselli, L., Popesso, P., Erfanianfar, G., & Concas, A. 2017, A&A, 597, A97, doi: 10.1051/0004-6361/201629409 Mu˜ noz L´ opez, C., Krajnovi´ c, D., Epinat, B., et al. 2025, arXiv e-prints, arXiv:2509.01710, doi: 10.48550/arXiv.2509.01710

    Pith/arXiv arXiv doi:10.48550/arxiv.2509.01710 2017

  69. [74]

    Murray, N., Quataert, E., & Thompson, T. A. 2005, ApJ, 618, 569, doi: 10.1086/426067

    doi:10.1086/426067 2005

  70. [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 18

    doi:10.1093/mnras/sty618 2018

  71. [76]

    2018, MNRAS, 475, 624, doi: 10.1093/mnras/stx3040 —

    Nelson, D., Pillepich, A., Springel, V., et al. 2018, MNRAS, 475, 624, doi: 10.1093/mnras/stx3040 —. 2019a, MNRAS, 490, 3234, doi: 10.1093/mnras/stz2306

    doi:10.1093/mnras/stx3040 2018

  72. [77]

    2019b, Computational Astrophysics and Cosmology, 6, 2, doi: 10.1186/s40668-019-0028-x

    Nelson, D., Springel, V., Pillepich, A., et al. 2019b, Computational Astrophysics and Cosmology, 6, 2, doi: 10.1186/s40668-019-0028-x

    doi:10.1186/s40668-019-0028-x

  73. [78]

    G., Weiner, B

    Noeske, K. G., Weiner, B. J., Faber, S. M., et al. 2007a, ApJL, 660, L43, doi: 10.1086/517926 —. 2007b, ApJL, 660, L43, doi: 10.1086/517926

    doi:10.1086/517926

  74. [79]

    E., Hayward, C

    Orr, M. E., Hayward, C. C., Nelson, E. J., et al. 2017, ApJL, 849, L2, doi: 10.3847/2041-8213/aa8f93

    doi:10.3847/2041-8213/aa8f93 2017

  75. [80]

    2015, ApJ, 807, 141, doi: 10.1088/0004-637X/807/2/141

    Pannella, M., Elbaz, D., Daddi, E., et al. 2015, ApJ, 807, 141, doi: 10.1088/0004-637X/807/2/141

    doi:10.1088/0004-637x/807/2/141 2015

  76. [81]

    J., Wang, L., Hurley, P

    Pearson, W. J., Wang, L., Hurley, P. D., et al. 2018, A&A, 615, A146, doi: 10.1051/0004-6361/201832821

    doi:10.1051/0004-6361/201832821 2018

  77. [82]

    2015, Nature, 521, 192, doi: 10.1038/nature14439

    Peng, Y., Maiolino, R., & Cochrane, R. 2015, Nature, 521, 192, doi: 10.1038/nature14439

    doi:10.1038/nature14439 2015

  78. [83]

    J., Kovaˇ c, K., et al

    Peng, Y.-j., Lilly, S. J., Kovaˇ c, K., et al. 2010, ApJ, 721, 193, doi: 10.1088/0004-637X/721/1/193 P´ erez-Gonz´ alez, P. G., Costantin, L., Langeroodi, D., et al. 2023, ApJL, 951, L1, doi: 10.3847/2041-8213/acd9d0

    doi:10.1088/0004-637x/721/1/193 2010

  79. [84]

    N., Taylor, A

    Perry, M. N., Taylor, A. J., Chavez Ortiz, O. A., et al. 2025, arXiv e-prints, arXiv:2510.05388, doi: 10.48550/arXiv.2510.05388

    doi:10.48550/arxiv.2510.05388 2025

  80. [85]

    2018a, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112

    Pillepich, A., Nelson, D., Hernquist, L., et al. 2018a, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112

    doi:10.1093/mnras/stx3112

Showing first 80 references.