The Effect of Ram Pressure on Star Formation
Bibliographic record
Abstract
Ram pressure stripping is a well-known galactic quenching mechanism capable of removing star-forming gas from a galaxy as it falls into a group or cluster. However, prior to stripping, ram pressure can induce brief periods of enhanced star formation by compressing the gas on the leading side of an infalling galaxy. Studies of this phenomenon have focused primarily on a unique population of galaxies for which a stripped tail of gas opposing the direction of motion is visible, known as jellyfish galaxies. The role of this effect in galaxy evolution overall is currently unknown. This thesis investigates the importance of ram pressure-induced star formation across all infalling galaxies to generalize our understanding of the effect. We use several metrics to measure the star formation asymmetries of a large sample of group and cluster galaxies in the Sloan Digital Sky Survey using $u$-band imaging from the Canada-France Imaging Survey as a tracer for star formation rate. We find that the distributions of star formation asymmetries of satellite galaxies are indistinguishable from those of a control sample of isolated field galaxies. Subdividing the sample by host halo mass and time since infall, we still find no environmental dependence of ram pressure as an enhancer of star formation. We conclude that any statistical star formation enhancement is small for infalling galaxies, suggesting that this effect is either uncommon or short-lived.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".