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arxiv: 2509.08040 · v2 · pith:LCUTQGFRnew · submitted 2025-09-09 · 🌌 astro-ph.GA · astro-ph.CO

COSMOS-Web galaxy groups: Evolution of red sequence and quiescent galaxy fraction

Greta Toni , Matteo Maturi , Gianluca Castignani , Lauro Moscardini , Ghassem Gozaliasl , Alexis Finoguenov , Sina Taamoli , B. Hollis Akins
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C. Rafael Arango-Toro M. Caitlin Casey E. Nicole Drakos L. Andreas Faisst Carter Flayhart Maximilien Franco Fabrizio Gentile Ali Hadi Santosh Harish Hossein Hatamnia Olivier Ilbert Shuowen Jin S. Jeyhan Kartaltepe Ali Ahmad Khostovan M. Anton Koekemoer Gavin Leroy E. Georgios Magdis Henry Joy McCracken Jed McKinney Louise Paquereau Jason Rhodes R. Michael Rich E. Brant Robertson M. Rasha Samir Diana Scognamiglio Samaneh Shamyati Marko Shuntov A. Jorge Zavala
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Pith reviewed 2026-05-22 13:08 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.CO
keywords galaxy groupsred sequencequiescent galaxiesstar formation quenchingredshift evolutionCOSMOS fieldAMICO groupsgalaxy clusters
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The pith

Quiescent galaxies accumulate steadily in groups from z=2 onward, faster in richer systems, while the red sequence ridgeline stabilizes early and shows no evolution over 12 Gyr.

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

The paper tracks how the fraction of galaxies that have stopped forming stars and the shape of the red sequence change inside galaxy groups as the universe ages from redshift 3.7 to today. Using photometric data from the COSMOS field and a machine-learning classifier that learns from traditional color and magnitude cuts, the authors measure rising quiescent fractions that appear earlier and grow quicker in the richest groups. They locate the first galaxies on the red-sequence ridgeline by z approximately 2 and find that the slope and width of that ridgeline remain steady across cosmic time. The work also notes lower quiescent fractions in X-ray faint groups, consistent with those systems sitting in less dense filament environments. These patterns support the idea that group-scale environments help shut down star formation on a predictable schedule.

Core claim

In AMICO-selected groups from the COSMOS-Web survey, quiescent galaxy fractions rise steadily from z=1.5-2 across all richness bins, with the most massive groups showing the earliest and fastest increase; the red-sequence ridgeline is already in place by z~2, and neither its slope nor its scatter changes measurably over the subsequent 12 Gyr; a rare overdensity of quiescent galaxies appears at z=3.4, while X-ray faint groups display lower average quiescent fractions than X-ray bright ones.

What carries the argument

Machine-learning classifier trained on rest-frame magnitudes that outputs a quiescent probability for each galaxy, combined with AMICO membership probabilities and sigma-clipped ridgeline fitting on photometric colors.

If this is right

  • Environmental processes inside groups must begin quenching star formation as early as z=2 and act more efficiently in richer systems.
  • Once galaxies reach the red sequence, their color evolution proceeds without measurable changes to the sequence's slope or scatter for the next 12 billion years.
  • X-ray faint groups, typically located in filaments, experience slower quenching than X-ray bright groups at the same redshift.
  • The presence of a quiescent overdensity at z=3.4 implies that some early red sequences can form in overdense regions well before z=2.

Where Pith is reading between the lines

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

  • Group environments appear to accelerate the transition to quiescence relative to the average field population at the same epoch.
  • The stability of red-sequence parameters may constrain how quickly stellar populations age after quenching without requiring large changes in dust or metallicity.
  • Repeating the same analysis in wider surveys could test whether the observed richness dependence scales directly with dark-matter halo mass.

Load-bearing premise

The machine-learning tool correctly separates quiescent from star-forming galaxies at all redshifts up to 3.7 without large contamination or incompleteness that would distort the measured fractions or ridgeline parameters.

What would settle it

A measurement showing quiescent fractions at z greater than 3 that are either much higher or much lower than the steady-buildup trend, or a clear change in red-sequence slope between z=2 and z=0 in the same group sample, would contradict the central claim.

Figures

Figures reproduced from arXiv: 2509.08040 by A. Jorge Zavala, Alexis Finoguenov, Ali Ahmad Khostovan, Ali Hadi, B. Hollis Akins, Carter Flayhart, C. Rafael Arango-Toro, Diana Scognamiglio, E. Brant Robertson, E. Georgios Magdis, E. Nicole Drakos, Fabrizio Gentile, Gavin Leroy, Ghassem Gozaliasl, Gianluca Castignani, Greta Toni, Henry Joy McCracken, Hossein Hatamnia, Jason Rhodes, Jed McKinney, L. Andreas Faisst, Lauro Moscardini, Louise Paquereau, M. Anton Koekemoer, Marko Shuntov, Matteo Maturi, Maximilien Franco, M. Caitlin Casey, M. Rasha Samir, Olivier Ilbert, R. Michael Rich, Samaneh Shamyati, Santosh Harish, Shuowen Jin, Sina Taamoli, S. Jeyhan Kartaltepe.

Figure 1
Figure 1. Figure 1: Feature importance for an initial XGB training, measured by the F-score (relative split frequency; orange bars), highlights bands directly used to define ground-truth labels are the most frequent in decision splits. To better assess feature impact, we evaluate SHAP scores (Lund￾berg & Lee 2017), which quantify the relative feature contribution to predictions (blue bars). Both approaches confirm that rest-f… view at source ↗
Figure 2
Figure 2. Figure 2: Calibration curves showing the bias between predicted probabil￾ity and fraction of positives (star-forming galaxies) using all rest-frame magnitude bands (solid lines) and the four most impacting in the de￾cision (dashed line). Different colors represent different methods and configurations, as in the legend. the decision-making process. This result is expected, as these bands are directly used in assignin… view at source ↗
Figure 3
Figure 3. Figure 3: Results of our probabilistic classification of COSMOS-Web galaxies with XGB with imputation, as in the NUVrJ (left column), NUVrK (middle column), and SFR–M (right column) planes. Each hexagon is color-coded by the mean quiescent probability (scale bar on the right), with rows corresponding to different redshift bins. As expected from our training, the method consistently reproduces classical cuts (white l… view at source ↗
Figure 4
Figure 4. Figure 4: Purity-weighted quiescent fraction vs. redshift in different rich￾ness (λ⋆) bins, as in the legend. 4.2. Cylinder background subtraction The AMICO algorithm produces model-dependent membership probabilities for the group member galaxies. In some cases, when quantities used to model the distribution of galaxies in the template are studied, this might lead to biased considerations due to the underlying model… view at source ↗
Figure 5
Figure 5. Figure 5: Purity-weighted quiescent fraction as estimated with the cylinder background subtraction, as a function of redshift for groups with λ⋆ > 10. Different colors show different richness bins, as in the legend. The gray trend lines refer to the two richest bins obtained with the pure membership method ( [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: as a function of redshift (top panel) and AMICO signal-to￾noise ratio, S/Nnocl (bottom panel) for two different richness bins (blue and orange lines). The result is that groups with clear X￾ray emission tend to have a quiescent fraction consistently higher than or at most equal to that of groups without significant X-ray emission. In particular, rich groups detected with high S/N and groups at z ≲ 0.5 seem… view at source ↗
Figure 7
Figure 7. Figure 7: Filament signal (ratio of number of filament galaxies to number of filament and cluster galaxies) probability density function for X-ray bright (Y) and X-ray faint (N) groups, using the LSS data and galaxy classification by Taamoli et al. in prep. Consistent results are obtained using the LSS information provided by Darvish et al. (2014) 2009), namely by choosing two magnitude bands that bracket a characte… view at source ↗
Figure 9
Figure 9. Figure 9: Top panel: fraction of the selected RS galaxies over the total (λRS /λ) in each group, as a function of redshift for the sample with identified red sequence. The yellow and blue lines indicate the mean fraction in each z-bin weighted by the group S/N (size of red points is proportional to the group S/N). Bottom panel: the fraction of groups with identified red sequence at different S/N (or purity) levels, … view at source ↗
Figure 10
Figure 10. Figure 10: Observed-frame CMD of an overdensity of galaxies at z = 3.4 (CW117) with 3 quiescent galaxies (red points with black contours) with colors consistent with RS evolutionary synthesis models for a typ￾ical m⋆ elliptical (orange line). This is the highest RS we detected. ment with, for instance, Mei et al. (2009); Cerulo et al. (2016). A typically adopted slope for the RS (Durret et al. 2011) is shown as a da… view at source ↗
Figure 11
Figure 11. Figure 11: The main red-sequence parameters vs redshift, from top to bot￾tom: the average observed color compared to our reference model (or￾ange lines), the slope, a zoom on the slope of RS-groups only, the scat￾ter for RS-groups only. Grey represents the full sample, red the RS-only groups in the observed frame, and blue in rest frame. – The AMICO algorithm identifies galaxy groups without re￾lying explicitly on c… view at source ↗
read the original abstract

We investigate the redshift and group richness dependence of the quiescent fraction and red-sequence (RS) parameters in COSMOS galaxy groups from z=0 to z=3.7. We analyzed the deep and well-characterized sample of groups detected with AMICO in the COSMOS(-Web) field. Our study of the quiescent galaxy population is based on a machine-learning classification tool based on rest-frame magnitudes. The algorithm learns from several traditional methods to estimate the probability of a galaxy being quiescent, achieving high precision and recall. Starting from this classification, we computed quiescent galaxy fractions within groups via two methods: one based on the membership probabilities provided by AMICO, which rely on an analytical model, and another using a model-independent technique. We then detected the RS by estimating the ridgeline position using photometric data, followed by sigma clipping to remove outliers. This analysis was performed using both rest-frame and observed-frame magnitudes with rest-frame matching. We compared the results from both approaches and investigated the $z$ and richness dependence of the RS parameters. We found that the quiescent galaxy population in groups builds up steadily from z=1.5-2 across all richnesses, with faster and earlier growth in the richest groups. The first galaxies settle onto the RS ridgeline by $z \sim 2$, consistent with current evolutionary scenarios. Notably, we reported a rare overdensity of quiescent galaxies at z=3.4, potentially one of the most distant early RSs observed. Extending our study to X-rays, we found that X-ray faint groups have, on average, lower quiescent fractions than X-ray bright ones, likely reflecting their typical location in filaments. Leveraging the broad wavelength coverage of COSMOS2025, we traced RS evolution over $\sim 12$ Gyr, finding no significant trends in either slope or scatter of the ridgeline.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper analyzes the redshift and richness dependence of quiescent galaxy fractions and red sequence (RS) parameters in AMICO-detected galaxy groups in the COSMOS field from z=0 to z=3.7. It uses a machine-learning classifier based on rest-frame magnitudes to determine quiescent probabilities, computes fractions with AMICO membership probabilities and a model-independent approach, and fits the RS ridgeline via sigma-clipping in rest- and observed-frame. The main results are a steady buildup of quiescent galaxies from z ≈ 1.5–2 (faster in richer groups), early RS settlement by z ∼ 2, no significant evolution in RS slope or scatter over ∼12 Gyr, and a reported quiescent overdensity at z=3.4.

Significance. If the central results hold, this study would provide key observational evidence for the gradual assembly of the red sequence in group environments over a wide redshift range, with implications for models of environmental quenching. Strengths include the use of two independent methods for quiescent fractions, sigma-clipping for robust RS detection, and leveraging the extensive COSMOS photometry for high-z reach. The comparison of X-ray bright and faint groups adds context on environmental effects.

major comments (1)
  1. [§3.2] The machine-learning classification tool is trained on rest-frame magnitudes from traditional methods; however, at z > 2.5, rest-frame colors rely on photometric redshifts with increased uncertainties and fewer bands. This raises the risk of redshift-dependent contamination or incompleteness that could bias the quiescent fractions (computed via AMICO probabilities) and the sigma-clipped RS parameters. A redshift-binned validation against spectroscopic data or mock catalogs is required to confirm the reported trends and the z=3.4 overdensity.
minor comments (2)
  1. [Abstract] The abstract states 'COSMOS(-Web) field'; clarifying the exact survey area and depth used would aid readers.
  2. Consider adding a table summarizing the RS fit parameters (slope, scatter, zero-point) across redshift bins for easier comparison.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful and constructive review of our manuscript. We address the major comment below and have revised the paper to strengthen the validation of our methods at high redshift.

read point-by-point responses

  1. Referee: [§3.2] The machine-learning classification tool is trained on rest-frame magnitudes from traditional methods; however, at z > 2.5, rest-frame colors rely on photometric redshifts with increased uncertainties and fewer bands. This raises the risk of redshift-dependent contamination or incompleteness that could bias the quiescent fractions (computed via AMICO probabilities) and the sigma-clipped RS parameters. A redshift-binned validation against spectroscopic data or mock catalogs is required to confirm the reported trends and the z=3.4 overdensity.

    Authors: We agree that explicit validation of the machine-learning classifier at z > 2.5 is warranted, as photometric redshift uncertainties and reduced band coverage can affect rest-frame color estimates. The classifier is trained to reproduce outputs from traditional methods that rely on the identical photometric inputs, which provides internal consistency, but we acknowledge this does not fully substitute for an independent check. In the revised manuscript we will add a dedicated subsection to §3.2 that performs a redshift-binned comparison of ML quiescent probabilities against available spectroscopic classifications in the COSMOS field for z > 2.5. We will also examine the photometric redshift quality and band coverage specifically for the galaxies contributing to the z=3.4 overdensity. These additions will directly address the potential for redshift-dependent bias in both the quiescent fractions and the RS parameters. revision: yes

Circularity Check

0 steps flagged

Observational measurements with no self-referential derivations

full rationale

This paper is a data-driven observational analysis of COSMOS galaxy groups. Quiescent fractions are computed directly from AMICO membership probabilities (analytical model) and a model-independent count, after applying an ML classifier trained on rest-frame magnitudes and traditional methods. RS ridgeline parameters come from sigma-clipping fits to photometric data in both rest-frame and observed-frame. No equations or steps reduce a claimed result to a fitted input by construction, no self-citations bear the load of the central evolutionary trends, and all quantities are externally falsifiable against the input catalog. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central results rest on the accuracy of the AMICO group finder, the ML quiescent classifier trained on rest-frame magnitudes, and the assumption that photometric redshifts and membership probabilities are unbiased across the full redshift range.

free parameters (1)
  • ML classifier decision threshold
    Probability cutoff for quiescent classification learned from training data; affects measured fractions.
axioms (1)
  • domain assumption AMICO membership probabilities accurately reflect true group membership without significant projection effects.
    Invoked when computing quiescent fractions via the analytical model method.

pith-pipeline@v0.9.0 · 6063 in / 1335 out tokens · 26517 ms · 2026-05-22T13:08:19.235987+00:00 · methodology

discussion (0)

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