Insights into environmental quenching at <i>z</i> ∼ 1: an enhancement of faint, low-mass passive galaxies in clusters
Bibliographic record
Abstract
ABSTRACT Understanding the processes that transform star-forming galaxies into quiescent ones is key to unravelling the role of environment in galaxy evolution. We present measurements of the luminosity functions (LFs) and stellar mass functions (SMFs) of passive red-sequence galaxies in four galaxy clusters at $0.8 < z < 1.3$, selected using deep Very Large Telescope (VLT) observations complemented with data from the Gemini CLuster Astrophysics Spectroscopic (GCLASS) and Gemini Observations of Galaxies in Rich Early ENvironments (GOGREEN) surveys. We find a significant enhancement in the abundance of faint/low-mass passive galaxies in both the LFs and SMFs of all four clusters compared to the field. This is further evidenced by a shallower low-mass slope in the composite passive cluster SMF, which yields a Schechter parameter $\alpha = -0.54^{+\, 0.03}_{-0.03}$, compared to $\alpha = 0.12^{+\, 0.01}_{-0.01}$ for the field. Our findings indicate that quenching processes that act in clusters are enhanced compared to the field, suggesting that environmental quenching mechanisms may already be active by $z\sim 1$. To reproduce the observed passive cluster SMF, we estimate that $25\pm 5~{{\ \rm per\ cent}}$ of the star-forming field population that falls into the cluster must have been quenched. Our results largely support traditional quenching models but highlight the need for deeper studies of larger cluster samples to better understand the role of environmental quenching in the distant Universe.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".