Dependence of galaxy quenching on halo mass and distance from its centre
Bibliographic record
Abstract
We study the dependence of star formation quenching on galaxy mass and environment, in the Sloan Digital Sky Survey (SDSS; z ∼ 0.1) and the All-Wavelength Extended Groth Strip International Survey (AEGIS; z ∼ 1). It is crucial that we define quenching by low star formation rate rather than by red colour, given that one-third of the red galaxies are star forming. We address stellar mass M*, halo mass Mh, density over the nearest N neighbours δN and distance to the halo centre D. The fraction of quenched galaxies appears more strongly correlated with Mh at fixed M* than with M* at fixed Mh, while for satellites quenching also depends on D. We present the M*–Mh relation for centrals at z ∼ 1. At z ∼ 1, the dependence of quenching on M* at fixed Mh is somewhat more pronounced than at z ∼ 0, but the quenched fraction is low (10 per cent) and the haloes are less massive. For satellites, M*-dependent quenching is noticeable at high D, suggesting a quenching dependence on subhalo mass for recently captured satellites. At small D, where satellites likely fell in more than a few Gyr ago, quenching strongly depends on Mh and not on M*. The Mh dependence of quenching is consistent with theoretical wisdom where virial shock heating in massive haloes shuts down accretion and triggers ram-pressure stripping, causing quenching. The interpretation of δN is complicated by the fact that it depends on the number of observed group members compared to N, motivating the use of D as a better measure of local environment.
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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 | 0.001 |
| 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".