The Pan-STARRS1 Medium-deep Survey: Star Formation Quenching in Group and Cluster Environments
Bibliographic record
Abstract
Abstract We make use of a catalog of 1600 Pan-STARRS1 groups produced by the probability friends-of-friends algorithm to explore how the galaxy properties, i.e., the specific star formation rate (SSFR) and quiescent fraction, depend on stellar mass and group-centric radius. The work is the extension of Lin et al. In this work, powered by a stacking technique plus a background subtraction for contamination removal, a finer correction and more precise results are obtained than in our previous work. We find that while the quiescent fraction increases with decreasing group-centric radius, the median SSFRs of star-forming galaxies in groups at fixed stellar mass drop slightly from the field toward the group center. This suggests that the main quenching process in groups is likely a fast mechanism. On the other hand, a reduction in SSFRs by ∼0.2 dex is seen inside clusters as opposed to the field galaxies. If the reduction is attributed to the slow quenching effect, the slow quenching process acts dominantly in clusters. In addition, we also examine the density–color relation, where the density is defined by using a sixth-nearest-neighbor approach. Comparing the quiescent fractions contributed from the density and radial effect, we find that the density effect dominates the massive group or cluster galaxies, and the radial effect becomes more effective in less massive galaxies. The results support mergers and/or starvation as the main quenching mechanisms in the group environment, while harassment and/or starvation dominate in clusters.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".