The impact of cluster mergers on galaxy properties
Bibliographic record
Abstract
Abstract The impact of galaxy cluster mergers on the properties of the resident galaxies remains poorly understood. In this paper, we investigate the effects of merging environments on star formation (SF) activity in nearby clusters ( $0.04\lt z\lt0.06$ ) from the SAMI Galaxy Survey – A168, A2399, A3880, and EDCC 0442 – which exhibit different dynamical activity. Using single-fibre spectroscopy from the SAMI Cluster Redshift Survey and Sloan Digital Sky Survey, we trace SF activity across the cluster sample by identifying the star-forming galaxy (SFG) population based on spectral features. We find a mild enhancement in the star-forming galaxy fraction ( $f_{SFG}$ ) in merging clusters, although not statistically significant. The spatial and projected phase-space distributions show that SFGs in merging clusters are well-mixed with the passive population, while galaxy populations exhibit a clear segregation in the relaxed clusters. Analysis of the equivalent width of the H $\alpha$ line, as a tracer of recent SF activity, does not reveal strong evidence of triggered SF activity as a function of dynamical state for both the global cluster environment and subsamples of galaxies selected near possible merger features. This suggests that the increase in $f_{SFG}$ is due to the mixing of galaxies in dynamically complex, young merging systems that are still forming, unlike their older, relaxed counterparts that have had longer to quench.
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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 | 0.001 | 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".