STAR-FORMING GALAXY EVOLUTION IN NEARBY RICH CLUSTERS
Bibliographic record
Abstract
Dense environments are known to quench star formation in galaxies, but it is still unknown what mechanism(s) are directly responsible. In this paper, we study the star formation of galaxies in A2029 and compare it to that of Coma, combining indicators at 24 μm, Hα, and UV down to rates of 0.03 M ☉ yr −1 . We show that A2029's star-forming galaxies follow the same mass–SFR relation as the field. The Coma cluster, on the other hand, has a population of galaxies with star formation rates (SFRs) significantly lower than the field mass–SFR relation, indicative of galaxies in the process of being quenched. Over half of these galaxies also host active galactic nuclei. Ram-pressure stripping and starvation/strangulation are the most likely mechanisms for suppressing the star formation in these galaxies, but we are unable to disentangle which is dominating. The differences we see between the two clusters' populations of star-forming galaxies may be related to their accretion histories, with A2029 having accreted its star-forming galaxies more recently than Coma. Additionally, many early-type galaxies in A2029 are detected at 24 μm and/or in the far-UV, but this emission is not directly related to star formation. Similar galaxies have probably been classified as star forming in previous studies of dense clusters, possibly obscuring some of the effects of the cluster environment on true star-forming galaxies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".