The effect of cluster dynamical state on ram-pressure stripping
Bibliographic record
Abstract
ABSTRACT Theoretical and observational studies have suggested that ram-pressure stripping (RPS) by the intracluster medium can be enhanced during cluster interactions, boosting the formation of the ‘jellyfish’ galaxies. In this work, we study the incidence of galaxies undergoing RPS in 52 clusters of different dynamical states. We use optical data from the WINGS/OmegaWINGS surveys and archival X-ray data to characterize the dynamical state of our cluster sample, applying eight different proxies. We then compute the number of RPS candidates relative to the infalling population of blue late-type galaxies within a fixed circular aperture in each cluster. We find no clear correlation between the fractions of RPS candidates and the different cluster dynamical state proxies considered. These fractions also show no apparent correlation with cluster mass. To construct a dynamical state classification closer to a merging ‘sequence’, we perform a visual classification of the dynamical states of the clusters, combining information available in optical, X-ray, and radio wavelengths. We find a mild increase in the RPS fraction in interacting clusters with respect to all other classes (including post-mergers). This mild enhancement could hint at a short-lived enhanced RPS in ongoing cluster mergers. However, our results are not statistically significant due to the low galaxy numbers. We note this is the first homogeneous attempt to quantify the effect of cluster dynamical state on RPS using a large cluster sample, but even larger (especially wider) multiwavelength surveys are needed to confirm the results.
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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 | 0.004 |
| 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) | 0.002 | 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".