Quenching Low-mass Satellite Galaxies: Evidence for a Threshold ICM Density
Bibliographic record
Abstract
Abstract We compile a sample of Sloan Digital Sky Survey (SDSS) galaxy clusters with high-quality Chandra X-ray data to directly study the influence of the dense intracluster medium (ICM) on the quenching of satellite galaxies. We study the quenched fractions of satellite galaxies as a function of ICM density for low- (109 ≲ M ⋆ ≲ 1010 M ⊙), intermediate- (1010 ≲ M ⋆ ≲ 1010.5 M ⊙), and high-mass (M ⋆ ≳ 1010.5 M ⊙) satellite galaxies with >3000 satellite galaxies across 24 low-redshift (z < 0.1) clusters. For low-mass galaxies we find evidence for a broken power-law trend between satellite quenched fraction and local ICM density. The quenched fraction increases modestly at ICM densities below a threshold before increasing sharply beyond this threshold toward the cluster center. We show that this increase in quenched fraction at high ICM density is well matched by a simple, analytic model of ram pressure stripping. These results are consistent with a picture where low-mass cluster galaxies experience an initial, slow-quenching mode driven by steady gas depletion, followed by rapid quenching associated with ram pressure of cold-gas stripping near (one-quarter of the virial radius, on average) the cluster center.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".