The GOGREEN survey: constraining the satellite quenching time-scale in massive clusters at <i>z</i> ≳ 1
Bibliographic record
Abstract
ABSTRACT We model satellite quenching at z ∼ 1 by combining 14 massive (1013.8 < Mhalo/M⊙ < 1015) clusters at 0.8 < z < 1.3 from the GOGREEN and GCLASS surveys with accretion histories of 56 redshift-matched analogues from the IllustrisTNG simulation. Our fiducial model, which is parametrized by the satellite quenching time-scale (τquench), accounts for quenching in our simulated satellite population both at the time of infall by using the observed coeval field quenched fraction and after infall by tuning τquench to reproduce the observed satellite quenched fraction versus stellar mass trend. This model successfully reproduces the observed satellite quenched fraction as a function of stellar mass (by construction), projected cluster-centric radius, and redshift and is consistent with the observed field and cluster stellar mass functions at z ∼ 1. We find that the satellite quenching time-scale is mass dependent, in conflict with some previous studies at low and intermediate redshift. Over the stellar mass range probed (M⋆ > 1010 M⊙), we find that the satellite quenching time-scale decreases with increasing satellite stellar mass from ∼1.6 Gyr at 1010 M⊙ to ∼0.6−1 Gyr at 1011 M⊙ and is roughly consistent with the total cold gas (HI + H2) depletion time-scales at intermediate z, suggesting that starvation may be the dominant driver of environmental quenching at z < 2. Finally, while environmental mechanisms are relatively efficient at quenching massive satellites, we find that the majority ($\sim 65{\!-\!}80{{\ \rm per\ cent}}$) of ultra-massive satellites (M⋆ > 1011 M⊙) are quenched prior to infall.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".