The colours of satellite galaxies in groups and clusters
Bibliographic record
Abstract
Current models of galaxy formation predict satellite galaxies in groups and clusters that are redder than observed. We investigate the effect on the colours of satellite galaxies produced by the ram-pressure stripping of their hot-gaseous atmospheres as the satellites orbit within their parent halo. We incorporate a model of the stripping process based on detailed hydrodynamic simulations within the Durham semi-analytic model of galaxy formation. The simulations show that the environment in groups and clusters is less aggressive than previously assumed. The main uncertainty in the model is the treatment of gas expelled by supernovae. With reasonable assumptions for the stripping of this material, we find that satellite galaxies are able to retain a significant fraction of their hot gas for several Gyr, thereby replenishing their reservoirs of cold, star-forming gas and remaining blue for a relatively long period of time. A bimodal distribution of galaxy colours, similar to that observed in Sloan Digital Sky Survey data, is established and the colours of the satellite galaxies are in good agreement with the data. In addition, our model naturally accounts for the observed dependence of satellite colours on environment, from small groups to high-mass 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 | 0.002 |
| 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) | 0.001 | 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".