A homogeneous measurement of the delay between the onsets of gas stripping and star formation quenching in satellite galaxies of groups and clusters
Bibliographic record
Abstract
ABSTRACT We combine orbital information from N-body simulations with an analytic model for star formation quenching and SDSS observations to infer the differential effect of the group/cluster environment on star formation in satellite galaxies. We also consider a model for gas stripping, using the same input supplemented with H i fluxes from the ALFALFA survey. The models are motivated by and tested on the Hydrangea cosmological hydrodynamical simulation suite. We recover the characteristic times when satellite galaxies are stripped and quenched. Stripping in massive ($M_{\rm vir}\sim 10^{14.5}\, {\rm M}_\odot$) clusters typically occurs at or just before the first pericentric passage. Lower mass ($\sim 10^{13.5}\, {\rm M}_\odot$) groups strip their satellites on a significantly longer (by $\sim 3\, {\rm Gyr}$) time-scale. Quenching occurs later: Balmer emission lines typically fade $\sim 3.5\, {\rm Gyr}$ ($5.5\, {\rm Gyr}$) after first pericentre in clusters (groups), followed a few hundred Myr later by reddenning in (g − r) colour. These ‘delay time-scales’ are remarkably constant across the entire satellite stellar mass range probed (∼109.5–$10^{11}\, {\rm M}_\odot$), a feature closely tied to our treatment of ‘group pre-processing’. The lowest mass groups in our sample ($\sim 10^{12.5}\, {\rm M}_\odot$) strip and quench their satellites extremely inefficiently: typical time-scales may approach the age of the Universe. Our measurements are qualitatively consistent with the ‘delayed-then-rapid’ quenching scenario advocated for by several other studies, but we find significantly longer delay times. Our combination of a homogeneous analysis and input catalogues yields new insight into the sequence of events leading to quenching across wide intervals in host and satellite mass.
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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 | 0.000 | 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".