THE SUPPRESSION OF STAR FORMATION AND THE EFFECT OF THE GALAXY ENVIRONMENT IN LOW-REDSHIFT GALAXY GROUPS
Bibliographic record
Abstract
Understanding the interaction between galaxies and their surroundings is central to building a coherent picture of galaxy evolution. Here we use Galaxy Evolution Explorer imaging of a statistically representative sample of 23 galaxy groups at z ≈ 0.06 to explore how local and global group environments affect the UV properties and dust-corrected star formation rates (SFRs) of their member galaxies. The data provide SFRs out to beyond 2 R 200 in all groups, down to a completeness limit and limiting galaxy stellar mass of 0.06 M ☉ yr −1 and 1 × 10 8 M ☉ , respectively. At fixed galaxy stellar mass, we find that the fraction of star-forming group members is suppressed relative to the field out to an average radius of R ≈ 1.5 Mpc ≈2 R 200 , mirroring results for massive clusters. For the first time, we also report a similar suppression of the specific SFR within such galaxies, on average by 40% relative to the field, thus directly revealing the impact of the group environment in quenching star formation within infalling galaxies. At fixed galaxy density and stellar mass, this suppression is stronger in more massive groups, implying that both local and global group environments play a role in quenching. The results favor an average quenching timescale of ≳ 2 Gyr and strongly suggest that a combination of tidal interactions and starvation is responsible. Despite their past and ongoing quenching, galaxy groups with more than four members still account for at least ∼25% of the total UV output in the nearby universe.
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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 | 0.001 |
| 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 | 0.001 |
| 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".