THE PHASE SPACE AND STELLAR POPULATIONS OF CLUSTER GALAXIES AT<i>z</i>∼ 1: SIMULTANEOUS CONSTRAINTS ON THE LOCATION AND TIMESCALE OF SATELLITE QUENCHING
Bibliographic record
Abstract
We investigate the velocity vs. position phase space of z ~ 1 cluster galaxies using a set of 424 spectroscopic redshifts in 9 clusters drawn from the GCLASS survey. Dividing the galaxy population into three categories: quiescent, star-forming, and poststarburst, we find that these populations have distinct distributions in phase space. Most striking are the poststarburst galaxies, which are commonly found at small clustercentric radii with high clustercentric velocities, and appear to trace a coherent ``ring" in phase space. Using several zoom simulations of clusters we show that the coherent distribution of the poststarbursts can be reasonably well-reproduced using a simple quenching scenario. Specifically, the phase space is best reproduced if satellite quenching occurs on a rapid timescale (0.1 < tau_{Q} < 0.5 Gyr) after galaxies make their first passage of R ~ 0.5R_{200}, a process that takes a total time of ~ 1 Gyr after first infall. We compare this quenching timescale to the timescale implied by the stellar populations of the poststarburst galaxies and find that the poststarburst spectra are well-fit by a rapid quenching (tau_{Q} = 0.4^{+0.3}_{-0.4} Gyr) of a typical star-forming galaxy. The similarity between the quenching timescales derived from these independent indicators is a strong consistency check of the quenching model. Given that the model implies satellite quenching is rapid, and occurs well within R_{200}, this would suggest that ram-pressure stripping of either the hot or cold gas component of galaxies are the most plausible candidates for the physical mechanism. The high cold gas consumption rates at z ~ 1 make it difficult to determine if hot or cold gas stripping is dominant; however, measurements of the redshift evolution of the satellite quenching timescale and location may be capable of distinguishing between the two.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".