The Evolution of Environmental Quenching Timescales to z ∼ 1.6: Evidence for Dynamically Driven Quenching of the Cluster Galaxy Population
Bibliographic record
Abstract
Abstract Using a sample of four galaxy clusters at 1.35 < z < 1.65 and 10 galaxy clusters at 0.85 < z < 1.35, we measure the environmental quenching timescale, t Q, corresponding to the time required after a galaxy is accreted by a cluster for it to fully cease star formation. Cluster members are selected by a photometric-redshift criterion, and categorized as star-forming, quiescent, or intermediate according to their dust-corrected rest-frame colors and magnitudes. We employ a “delayed-then-rapid” quenching model that relates a simulated cluster mass accretion rate to the observed numbers of each type of galaxy in the cluster to constrain t Q. For galaxies of mass M * ≳ 1010.5 M ⊙, we find a quenching timescale of t Q = Gyr in the z ∼ 1.5 cluster sample, and Gyr at z ∼ 1. Using values drawn from the literature, we compare the redshift evolution of t Q to timescales predicted for different physical quenching mechanisms. We find t Q to depend on host halo mass such that quenching occurs over faster timescales in clusters relative to groups, suggesting that properties of the host halo are responsible for quenching high-mass galaxies. Between z = 0 and z = 1.5, we find that t Q evolves faster than the molecular gas depletion timescale and slower than an estimated star formation rate-outflow timescale, but is consistent with the evolution of the dynamical time. This suggests that environmental quenching in these galaxies is driven by the motion of satellites relative to the cluster environment, although due to uncertainties in the atomic gas budget at high redshift, we cannot rule out quenching due to simple gas depletion.
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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 | 0.002 |
| 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 | 0.001 |
| 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".