Removal and mixing of the coronal gas from satellites in galaxy groups: cooling the intragroup gas
Bibliographic record
Abstract
The existence of an extended hot gaseous corona surrounding clusters, groups and massive galaxies is well established by observational evidence and predicted by current theories of galaxy formation. When a small galaxy collides with a larger one, their coronae are the first to interact, producing disturbances that remove gas from the smaller system and settle it into the corona of the larger one. For a Milky Way sized galaxy merging into a low-mass group, ram pressure stripping and the Kelvin–Helmholtz instability are the most relevant of these disturbances. We argue that the turbulence generated by the latter mixes the material of both coronae in the wake of the orbiting satellite creating a ‘warm phase’ mixture with a cooling time a factor of several shorter than that of the ambient intragroup gas. We reach this conclusion using analytic estimates, as well as adiabatic and dissipative high-resolution numerical simulations of a spherical corona subject to the ablation process of a constant velocity wind with uniform density and temperature. Although this is a preliminary analysis, our results are promising and we speculate that the mixture could potentially trigger in situ star formation and/or be accreted into the central galaxy as a cold gas flow resulting in a new mode of star formation in galaxy groups and 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".