Satellite quenching was not important for <i>z</i> ∼ 1 clusters: most quenching occurred during infall
Bibliographic record
Abstract
ABSTRACT We quantify the relative importance of environmental quenching versus pre-processing in z ∼ 1 clusters by analysing the infalling galaxy population in the outskirts of 15 galaxy clusters at 0.8 < z < 1.4 drawn from the GOGREEN and GCLASS surveys. We find significant differences between the infalling galaxies and a control sample; in particular, an excess of massive quiescent galaxies in the infalling region. These massive infalling galaxies likely reside in larger dark matter haloes than similar-mass control galaxies because they have twice as many satellite galaxies. Furthermore, these satellite galaxies are distributed in an NFW profile with a larger scale radius compared to the satellites of the control galaxies. Based on these findings, we conclude that it may not be appropriate to use ‘field’ galaxies as a substitute for infalling pre-cluster galaxies when calculating the efficiency and mass dependence of environmental quenching in high-redshift clusters. By comparing the quiescent fraction of infalling galaxies at 1 < R/R200<3 to the cluster sample (R/R200<1) we find that almost all quiescent galaxies with masses >1011 M⊙ were quenched prior to infall, while up to half of lower mass galaxies were environmentally quenched after passing the virial radius. This means most of the massive quiescent galaxies in z ∼ 1 clusters were self-quenched or pre-processed 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".