Ram pressure candidates in UNIONS
Bibliographic record
Abstract
ABSTRACT We present a search for disturbed, candidate ram pressure stripping galaxies across more than 50 spectroscopically selected SDSS groups and clusters. 48 ram pressure candidates are visually identified in these systems using high-quality UNIONS imaging from the Canada-France-Hawaii Telescope, covering ${\sim }6200\, \mathrm{deg^2}$ and ${\sim }2800\, \mathrm{deg^2}$ in the u- and r-bands, respectively. Ram pressure candidates are found in groups and clusters spanning a wide range in halo mass and include ∼30 ram pressure candidates in the group regime (Mh < 1014). The observed frequency of ram pressure candidates shows substantial scatter with group/cluster mass, but on average is larger in clusters ($M_h \ge 10^{14}\, \mathrm{M_\odot }$) than groups ($M_h \lt 10^{14}\, \mathrm{M_\odot }$) by a factor of ∼2. We find that ram pressure candidates are most commonly low-mass galaxies and have enhanced star formation rates relative to star-forming field galaxies. The enhancement in star formation is largely independent of galaxy mass and strongest for galaxies in clusters. As a result of the large survey footprint and excellent image quality from UNIONS, we are able to identify disturbed galaxies, potentially affected by ram pressure stripping, across a wide range of host environment.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".