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REVIEW 3 major objections 4 minor 114 references

Environmental vs. intrinsic quenching at cosmic noon: Predictions from cosmological hydrodynamical simulations for VLT-MOONRISE

T0 review · 3 major / 4 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read Two leading cosmological simulations agree on the physics of galaxy quenching but disagree sharply on what the VLT-MOONRISE survey will see at cosmic noon.

desk verdict Useful MOONRISE-era predictions from a familiar RF pipeline, but the boldest EAGLE claim contradicts the paper's own Table 2 and needs a hard look before it is taken at face value. read the letter →

arxiv 2509.09626 v1 pith:UZ2OEN3Z submitted 2025-09-11 astro-ph.GA astro-ph.CO

Paul H. Goubert , Asa F. L. Bluck , Joanna M. Piotrowska , Paul Torrey , Roberto Maiolino , Thomas Pinto Franco , Camilo Casimiro , Nicolas Cea This is my paper
classification astro-ph.GAastro-ph.CO
keywords galaxyquenchingcosmicnoonVLT-MOONRISEIllustrisTNGEAGLEsimulationRandomForestclassificationsatellitegalaxiesAGNfeedback
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that in both IllustrisTNG and EAGLE, quenching follows two distinct paths: central galaxies and high-mass satellites stop forming stars because of supermassive black hole growth (AGN feedback), while low-mass satellites quench because of their group environment, best traced by halo mass. This split holds from z=0 to z=2. The authors build 'MOONRISE-Like' samples that mimic the upcoming VLT-MOONRISE survey's mass limit, completeness, and observer geometry, and produce a stark testable disagreement: TNG predicts MOONRISE will see environmentally quenched low-mass satellites at cosmic noon, EAGLE predicts essentially none. A secondary finding is that EAGLE shows galaxies re-igniting star formation at late times while TNG does not, which the paper ties to the presence or absence of a preventative AGN feedback mode. A sympathetic reader should care because MOONRISE can directly rule on which simulation's quenching physics matches nature.

What carries the argument

The central machinery is a Random Forest classifier applied to simulated galaxies, with input features including black hole mass, halo mass, stellar mass, local over-density, and distance to central. The classifier identifies the most causally predictive parameter for quiescence while controlling for inter-correlations. This is combined with a bespoke mass-threshold method that locates the local minimum of the gradient of a Schechter fit to the quenched satellite stellar mass function, adds a 0.2 dex buffer, and splits satellites into low- and high-mass classes. The 'MOONRISE-Like' sample construction applies the survey's mass completeness limit, a 2D+z observer space, and 70% completeness t

What would settle it

If VLT-MOONRISE finds a significant population of quenched low-mass satellites (log M*/M_sun ~9.5-10) in groups at z~1-2, EAGLE's prediction fails; if it finds essentially none, TNG's prediction fails. Concretely, counting quenched low-mass satellites above the survey's mass completeness limit in the first MOONRISE data release would settle which simulated prediction is right.

Watch

Extended reading notes

Core claim

Using Random Forest classification on simulated galaxies at z=0, 1, and 2, the authors find that supermassive black hole mass is the best predictor of quiescence for central galaxies and high-mass satellites, while group halo mass is the best predictor for low-mass satellites, at every epoch. The split between low- and high-mass satellites is defined by a bespoke mass threshold derived from the bimodality of the quenched satellite stellar mass function, with a 0.2 dex buffer. The starkest discrepancy between the two simulations is in the mass threshold analysis: IllustrisTNG predicts environmentally quenched low-mass satellites that would be visible within VLT-MOONRISE's survey limits, where

Load-bearing premise

The paper assumes that the split between low- and high-mass satellites, derived from the fitted dip in the quenched satellite stellar mass function plus a 0.2 dex buffer, cleanly separates environmental from intrinsic quenching; if that dip is an artifact of simulations over-quenching low-mass satellites, the EAGLE/TNG prediction difference could reverse.

Editorial extensions

If this is right

  • If VLT-MOONRISE detects a population of quenched low-mass satellites at z~1-2, it would validate the TNG prediction and challenge EAGLE's quenching prescription.
  • If VLT-MOONRISE finds essentially no environmentally quenched low-mass satellites above its mass limit, the EAGLE prediction would be favored over TNG.
  • Measurements of quenched fraction versus black hole mass can distinguish TNG's preventative 'kinetic mode' AGN feedback from EAGLE's thermal feedback, especially through the presence or absence of rejuvenation.
  • The Random Forest results imply that halo mass, rather than local density alone, is the superior environmental predictor; accurate halo mass estimates in MOONRISE will be essential.
  • The study's mass-threshold method yields a specific, testable feature in the quenched stellar mass function: a dip separating the environmental and intrinsic quenching regimes that should appear if the bimodality is real.

Reading between the lines

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

  • If MOONRISE finds a significant abundance of quenched low-mass satellites, it would indirectly support TNG-style preventative AGN feedback and challenge EAGLE's thermal feedback; the opposite would support EAGLE. This is an editorial inference, not stated in the paper.
  • The paper implicitly suggests that density-only environmental studies at cosmic noon may misclassify high-mass satellite quenching as environmental when the true driver is black hole mass; one could test this by running the same Random Forest analysis with and without density as an input on MOONRISE data.
  • The mass-threshold method could be applied to other cosmological simulations or to the observed quenched mass function to check whether the bimodality is a robust feature or an artifact of simulation over-quenching; if it is artificial, the threshold-based predictions would need revision.
  • A testable extension is to measure the quenched fraction of low-mass satellites as a function of distance to the central galaxy and halo mass in MOONRISE, which would directly probe the ram-pressure, starvation, and interaction mechanisms the paper identifies as likely drivers.
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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 / 4 minor

Summary. This paper uses the IllustrisTNG and EAGLE simulations at z=0, 1, and 2 to identify which physical properties best predict quiescence for centrals, high-mass satellites, and low-mass satellites, with an eye toward VLT-MOONRISE. After constructing a 'MOONRISE-Like' sample (M*>10^9.5 Msun, 2D+z observer space, 70% completeness), the authors apply Random Forest classification to rank M_BH, M_*, M_Halo, delta, and D_cen. They find that M_BH dominates for centrals and high-mass satellites, while M_Halo dominates for low-mass satellites. They also derive redshift-dependent mass thresholds by fitting Schechter functions to quenched satellite stellar mass functions, and use these to claim a key divergence: TNG predicts environmentally quenched low-mass satellites visible to MOONRISE, whereas EAGLE predicts essentially none. The paper closes with a series of testable predictions for MOONRISE, including a possible distinction between EAGLE and TNG in the abundance of low-mass quenched satellites at cosmic noon.

Significance. If the central predictions were robust, this would be a timely and useful paper for planning MOONRISE science. Its strengths are the construction of survey-like samples, transparent reporting of Random Forest hyperparameters (Tables C1-C8), the use of two state-of-the-art simulations, and multiple complementary analyses (quenched fractions, Delta f_Q, radial trends). However, one of the headline predictions is internally contradicted by the paper's own Table 2, and the class-split methodology is at risk of circularity because the satellite mass thresholds are explicitly designed to separate the two quenching mechanisms and are then used to demonstrate that the mechanisms differ. With the internal contradiction corrected and the threshold sensitivity tested, the paper would provide a valuable comparative test of TNG versus EAGLE for MOONRISE.

major comments (3)
  1. [Sec. 4.1, Table 2, Fig. 6, Table C6] The text in Sec. 4.1 states that the EAGLE MOONRISE-Like samples at z>=1 are 'essentially devoid of low-mass satellites', and Fig. 6 and Sec. 4.3 repeat that no low-mass satellites are present at z=1,2. This is contradicted by Table 2: EAGLE z=1 MOONRISE-Like has N_LMS=406 and N_LMS,Q=45. With the z=1 bisecting mass of 9.85+/-0.2 (Table 3), the low-mass threshold after the 0.2 dex buffer is 9.65, so the sample contains galaxies with 9.5<log(M*/Msun)<9.65. Table C6 leaves the RF entries for EAGLE ML LM at z=1 and z=2 as ellipses. The claim that environmental quenching is 'undetectable' or that EAGLE predicts 'essentially none' is therefore not supported by the provided counts. Please re-run the z=1 low-mass satellite analysis, quantify expected counts in the MOONRISE volume, and revise the conclusion accordingly.
  2. [Sec. 4.1 and Abstract] Satellites are split using thresholds explicitly 'designed to separate environmental and intrinsic quenching mechanisms' (Abstract). The threshold is the minimum gradient of the quenched satellite SMF plus a 0.2 dex buffer (Sec. 4.1). The RF conclusion that low-mass satellites quench environmentally is then obtained from these very classes, so part of the result is baked into the sample definition. This matters because the low-mass upturn may be a simulation artifact (Kukstas et al. 2022, cited in Sec. 4.1). Please test the stability of the conclusions by varying the buffer (e.g., +/-0.1, +/-0.3 dex, and no buffer) and re-applying the RF and Delta f_Q analyses, or by using a continuous approach that does not preselect classes. Without such a test, the central dichotomy is not independent of the class definition.
  3. [Sec. 4.4, Figs. 9-10] The abstract and Sec. 4.4 claim 'strong evidence for the rejuvenation of star formation from z=2 to z=0 in EAGLE'. This is inferred from cross-sectional quenched fractions as a function of M_BH at three separate redshifts, not from tracking individual galaxies. Differential assembly histories could produce the same pattern without galaxies transitioning back to star formation. Please support the claim with merger-tree tracking of individual galaxies, or soften the wording to 'consistent with rejuvenation'.
minor comments (4)
  1. [Sec. 4.2, Sec. 4.3.1] Typos: 'uni-model' should be 'unimodal' in Sec. 4.2; 'wains' should be 'wanes' in Sec. 4.3.1.
  2. [Eq. (12), References] Eq. (12) appears to have a typo: p_i(n^2) should be p_i(n)^2. Also, the duplicated Pedregosa et al. (2011a,b) references should be merged.
  3. [Sec. 3.3, Sec. 5] Random Forest relative importance is referred to as 'causal' in several places. Since RF importance is a predictive/associational measure, I recommend rephrasing to avoid overclaiming causality.
  4. [Sec. 3.4, Eq. (14)] Please comment on the sensitivity of the sSFR quenching threshold to the -11.5 dex exclusion boundary and to the 3-sigma choice. This threshold controls all downstream quenched fractions and could affect the satellite counts in Table 2.

Circularity Check

2 steps flagged · score 5.0 of 10

Headline EAGLE/MOONRISE prediction reduces to the fitted mass threshold; QuenchE/I labels are definitional. Central RF results remain independent.

  1. self definitional [Section 4.1 (QuenchE/QuenchI definitions) and Section 4.5.1 (use of QuenchI evolution)]
    "The values of QuenchE and QuenchI are determined by evaluating the integral of the quenched Schechter function for the low and high-mass regimes (respectively), normalized by the total area under the quenched stellar mass function. ... for EAGLE QuenchI moves from 20% to 80% over the same redshift interval. This points to less effective environmental quenching at higher redshifts."

    Environmental and intrinsic quenching are not measured independently; they are defined by the fitted bisecting mass. All quenched galaxies below the threshold are counted as 'environmentally quenched' (QuenchE) and all above as 'intrinsically quenched' (QuenchI). Since the threshold itself was chosen to separate the two mechanisms, the QuenchE/QuenchI fractions and their redshift trends (e.g., QuenchI rising from 20% to 80% in EAGLE) are a restatement of the fitted threshold evolution (Table 3: EAGLE threshold drops from 10.30 to 9.55) rather than an independent empirical finding. The Random Forest analysis provides some independent validation, but the specific QuenchE/I trend is definitional.

  2. fitted input called prediction [Section 4.1, after Table 3, and Section 4.3 / Fig. 6 caption]
    "the low-mass thresholds at z = 1 & 2 (i.e., log(M*/M_sun) <= 9.65 and log(M*/M_sun) <= 9.35, respectively) lie near, or even below, the MOONRISE survey mass completeness limit. Therefore, the 'MOONRISE-Like' sample sets at z>=1 for EAGLE are essentially devoid of 'low-mass' satellites, i.e., satellites which are expected to quench solely via environmental means."

    The paper's starkest falsifiable prediction—that EAGLE predicts essentially no environmentally quenched low-mass satellites within MOONRISE—is an arithmetic consequence of the fitted bisecting mass (Table 3) combined with the MOONRISE mass cut (log M*/M_sun > 9.5). The 'low-mass satellite' class is defined by that fitted threshold, so the prediction reduces to 'the fitted threshold lies below the survey limit.' The +/-0.2 dex buffer is hand-chosen; shifting it would change the sample and could reverse the prediction. The claim of being 'essentially devoid' is also internally inconsistent with Table 2, which lists 406 EAGLE low-mass satellites (45 quenched) in the z=1 MOONRISE-Like sample. Thus the headline prediction is not an independent consequence of EAGLE's physics but a relabeling of

full rationale

The paper contains substantial independent analysis: the Random Forest classification is a genuine machine-learning measurement within each galaxy class, and the finding that black hole mass dominates for centrals/high-mass satellites while halo mass dominates for low-mass satellites is not logically forced by the mass-threshold construction. The self-citations (e.g., Goubert et al. 2024) are not load-bearing for these results. However, two parts of the argument are partially circular. First, the QuenchE/QuenchI fractions are defined by the mass threshold that was itself designed to separate environmental and intrinsic mechanisms, so trends in those fractions (e.g., 'environmental quenching weakens with redshift in EAGLE') are largely a restatement of the fitted threshold's evolution. Second, the headline MOONRISE prediction for EAGLE is essentially the fitted threshold compared with the survey mass limit; it is a prediction only in the weak sense that the fitted value is a model output. The internal inconsistency between Table 2 (406 EAGLE low-mass satellites at z=1) and the text 'essentially devoid' is a correctness/falsifiability concern rather than circularity, but it underscores how closely the prediction is tied to the threshold definition. Overall score 5: partial circularity affecting the headline EAGLE/MOONRISE claim, while the central RF results remain independent.

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

The central claims rest on fitted labels and thresholds: the sSFR quenching cut, the satellite mass thresholds, and the 0.2 dex buffer. The Random Forest results are not derived from these fits, but the headline EAGLE prediction about MOONRISE is essentially a consequence of the fitted threshold falling below the survey limit. No new physical entities are introduced.

free parameters (5)
  • sSFR quenching threshold per simulation and redshift = mu_SF - 3 sigma_SF from Gaussian fit (e.g., f_Q about 30% at z=0, <10% at z=2)
    Defines the quenched versus star-forming labels used in every analysis. Fit to the sSFR distribution after excluding log(sSFR)<-11.5.
  • Bisecting stellar mass thresholds for satellites = TNG: 10.30, 10.25, 10.15 at z=0,1,2; EAGLE: 10.30, 9.85, 9.55
    Split satellites into low-mass and high-mass classes from the minimum gradient of the quenched satellite stellar mass function. Directly determines whether MOONRISE-like samples contain low-mass satellites in EAGLE.
  • Buffer zone of +/-0.2 dex around bisecting mass = 0.2 dex
    Hand-chosen to reduce contamination between environmental and intrinsic quenching. The headline EAGLE prediction is sensitive to this choice because it pushes the effective low-mass cutoff below the MOONRISE completeness limit.
  • sSFR exclusion boundary for Gaussian fitting = log(sSFR/yr^-1) < -11.5
    Arbitrary cut to remove obviously quenched galaxies before fitting the star-forming peak; influences the mean and width of the star-forming Gaussian and hence the quenched fraction.
  • Random Forest hyperparameters (min_samples_leaf, train:test, balancing) = MSL values in Appendix C tables, e.g., 15-500
    Tuned by hand to maximize AUC and keep |Delta AUC| <= 0.07. They affect feature importance values, though the main conclusions appear stable across data sets.
assumptions (5)
  • domain assumption The bimodality of the quenched satellite stellar mass function reflects two physically distinct quenching channels: environmental at low mass and intrinsic at high mass.
    Invoked in Section 4.1 to justify the mass threshold split. If the low-mass upturn is an artifact of simulation over-quenching, the split and the EAGLE prediction are invalid.
  • domain assumption Random Forest feature importance, computed with all features evaluated at every node, identifies causal drivers of quenching rather than mere correlations.
    Used throughout the paper to claim AGN feedback causes quenching of centrals and high-mass satellites. Supported by earlier mock tests (Bluck et al. 2022) but not re-established here.
  • domain assumption The TNG and EAGLE simulations are sufficiently realistic to predict observable quenching populations at cosmic noon.
    The entire paper assumes the subgrid prescriptions for feedback, star formation, and black hole growth produce galaxy populations comparable to those MOONRISE will observe.
  • domain assumption The Gaussian fit to the star-forming sSFR peak gives an unbiased separation between quenched and star-forming galaxies at each epoch.
    Section 3.4 assumes a single Gaussian centered on the star-forming peak, excludes low-sSFR galaxies before fitting, and uses mean minus 3 sigma as the threshold. Green valley contamination is acknowledged.
  • domain assumption The MOONRISE-like sample construction (2D+z observer space, 70% completeness, M*>10^9.5) preserves the quenching signal relevant to the real survey.
    Section 3.1 applies these cuts and random removal without correction or simulation of survey selection function beyond them.

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Cite this review

Pith. "Pith review of Environmental vs. intrinsic quenching at cosmic noon: Predictions from cosmological hydrodynamical simulations for VLT-MOONRISE." pith.science (2026). https://pith.science/paper/UZ2OEN3Z

@misc{pith2026250909626,
  author       = {Pith},
  title        = {Pith review of: Environmental vs. intrinsic quenching at cosmic noon: Predictions from cosmological hydrodynamical simulations for VLT-MOONRISE},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UZ2OEN3Z}},
  note         = {Machine review of arXiv:2509.09626}
}
read the original abstract

We present an investigation into the quenching of simulated galaxies across cosmic time, honing in on the role played by both intrinsic and environmental mechanisms at different epochs. In anticipation of VLT-MOONRISE, the first wide-field spectroscopic galaxy survey to target cosmic noon, this work provides clear predictions to compare to the future observations. We investigate the quenching of centrals, high-mass satellites, and low-mass satellites from two cosmological hydrodynamical simulations: IllustrisTNG and EAGLE. Satellites are split according to bespoke mass thresholds, designed to separate environmental and intrinsic quenching mechanisms. To determine the best parameter for predicting quiescence, we apply a Random Forest classification analysis for each galaxy class at each epoch. The Random Forest classification determines supermassive black hole mass as the best predictor of quiescence in centrals and high-mass satellites. Alternatively, the quenching of low-mass satellites is best predicted by group halo mass, at all epochs. Additionally, we investigate the evolution in the dependence of the quenched fraction with various parameters, revealing a more complex picture. There is strong evidence for the rejuvenation of star formation from z = 2 to z = 0 in EAGLE, but not in IllustrisTNG. The starkest discrepancy between simulations rests in the mass threshold analysis. While IllustrisTNG predicts the existence of environmentally quenched satellites visible within the survey limits of MOONRISE, EAGLE does not. Hence, MOONRISE will provide critical data that is needed to evaluate current models, and constrain future models, of quenching processes.

Figures

Figures reproduced from arXiv: 2509.09626 by the authors.

Figure 1
Figure 1. Illustration of our method to determine the quenched threshold for galaxies from TNG (top panels) and EAGLE (bottom panels) at z = 0, 1, 2 (along each row). The panels show the sSFR distribution at each redshift for each simulation. Additionally, a Gaussian fit to the star forming population is overlaid (removing obviously quiescent systems at log(sSFR/yr−1 ) < −11.5). On each panel we present the mean sSFR of star … view at source ↗
Figure 2
Figure 2. The stellar mass functions of various galaxy populations for the TNG (Fig. 2a) and EAGLE (Fig. 2b) simulations. Explicitly, we show stellar mass functions for: (i) all galaxies (in black); (ii) quenched galaxies (in red); (iii) star forming galaxies (in blue); (iv) quenched satellites (in purple); and (v) quenched centrals (in orange). The rows correspond to a given simulation (TNG in the top row and EAGLE in the bo… view at source ↗
Figure 3
Figure 3. The distributions of log( 𝛿10 ) for the entire sample (shown in light grey), centrals (shown in blue), and satellites (shown in red) for the complete TNG data set (top panels) and the ‘MOONRISE-Like’ data set (bottom panels). We fit a single Gaussian to the uni-modal central distribution, and a double Gaussian to the bimodal satellite distribution. The mean of each Gaussian (𝜇1 & 𝜇2) of the satellites distributions … view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Identical in structure and method to [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: Random Forest quenching classification analyses for TNG. Each row corresponds to a galaxy class (from top to bottom: centrals, high-mass satellites, and low-mass satellites), and each column to a given redshift (z = 0 - 2, from left to right). The input features tested…
Figure 6
Figure 6. Figure 6: Random Forest quenching classification analyses for EAGLE. This figure is structured exactly like [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Random Forest quenching classification analyses for the TNG ‘MOONRISE-Like’ data set, absent 𝑀BH and 𝑀Halo. Each panel corresponds to a galaxy class (centrals, high-mass satellites, low-mass satellites), with the results at each redshift presented with different colore…
Figure 8
Figure 8. Figure 8: Random Forest quenching classification analyses for the EAGLE ‘MOONRISE-Like’ data set, absent 𝑀BH and 𝑀Halo. This figure is structured identically to [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: The quenched fraction of TNG central (shown in blue) and satellite (shown in violet) galaxies as a function of black hole mass for the ‘MOONRISE￾Like’ data set. The results are shown for different redshifts from left to right along each row (as labeled by the panel tit…
Figure 10
Figure 10. Figure 10: This figure is identical in structure to [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]
Figure 11
Figure 11. Figure 11: The quenching of satellites in TNG as a function stellar mass, separated by the mass of their parent halo (see legends). We present the delta quenched fraction (Δ 𝑓Q) as a function of stellar mass for the ‘MOONRISE-Like’ data sets. This statistic quantifies the enhanc…
Figure 12
Figure 12. Figure 12: Identical in structure to [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]
Figure 13
Figure 13. Figure 13: The quenching of low-mass satellites (‘LMS’ in titles) as a function of location within their groups or clusters for TNG. We present the delta quenched fraction (Δ 𝑓Q) as a function of the distance to the nearest central galaxy (𝐷cen) separated into bins of halo mass …

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    " 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.stat...

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.