When the well runs dry: modelling environmental quenching of high-mass satellites in massive clusters at <i>z</i> ≳ 1
Bibliographic record
Abstract
ABSTRACT We explore models of massive (>1010 M⊙) satellite quenching in massive clusters at z ≳ 1 using an MCMC framework, focusing on two primary parameters: Rquench (the host-centric radius at which quenching begins) and τquench (the time-scale upon which a satellite quenches after crossing Rquench). Our MCMC analysis shows two local maxima in the 1D posterior probability distribution of Rquench at approximately 0.25 and 1.0 R200. Analysing four distinct solutions in the τquench–Rquench parameter space, nearly all of which yield quiescent fractions consistent with observational data from the GOGREEN survey, we investigate whether these solutions represent distinct quenching pathways and find that they can be separated between ‘starvation’ and ‘core quenching’ scenarios. The starvation pathway is characterized by quenching time-scales that are roughly consistent with the total cold gas (H2 + H i) depletion time-scale at intermediate z, while core quenching is characterized by satellites with relatively high line-of-sight velocities that quench on short time-scales (∼0.25 Gyr) after reaching the inner region of the cluster (<0.30 R200). Lastly, we break the degeneracy between these solutions by comparing the observed properties of transition galaxies from the GOGREEN survey. We conclude that only the ‘starvation’ pathway is consistent with the projected phase-space distribution and relative abundance of transition galaxies at z ∼ 1. However, we acknowledge that ram pressure might contribute as a secondary quenching mechanism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".