Satellite quenching time-scales in clusters from projected phase space measurements matched to simulated orbits
Bibliographic record
Abstract
We measure the star formation quenching efficiency and time-scale in cluster environments. Our method uses N-body simulations to estimate the probability distribution of possible orbits for a sample of observed Sloan Digital Sky Survey galaxies in and around clusters based on their position and velocity offsets from their host cluster. We study the relationship between their star formation rates and their likely orbital histories via a simple model in which star formation is quenched once a delay time after infall has elapsed. Our orbit library method is designed to isolate the environmental effect on the star formation rate due to a galaxy's present-day host cluster from ‘pre-processing’ in previous group hosts. We find that quenching of satellite galaxies of all stellar masses in our sample (|$10^{9}{--}10^{11.5}{\rm \thinspace M_{{\odot }}}$|) by massive (|${>} 10^{13}{\rm \thinspace M_{{\odot}}}$|) clusters is essentially 100 per cent efficient. Our fits show that all galaxies quench on their first infall, approximately at or within a Gyr of their first pericentric passage. There is little variation in the onset of quenching from galaxy-to-galaxy: the spread in this time is at most ∼2 Gyr at fixed M*. Higher mass satellites quench earlier, with very little dependence on host cluster mass in the range probed by our sample.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".