Are compact groups hostile towards faint galaxies?
Bibliographic record
Abstract
Aims. The goal of this work is to understand whether the extreme environment of compact groups (CGs) can affect the distribution and abundance of faint galaxies around them. Methods. We performed an analysis of the faint galaxy population in the vicinity of compact and normal groups. We built a light-cone mock galaxy catalogue constructed from the Millennium Run Simulation II plus a semi-analytical model of galaxy formation. We identified a sample of CGs in the mock catalogue as well as a control sample of normal galaxy groups and computed the projected number density profiles of faint galaxies around the first and the second ranked galaxies. We also compared the profiles obtained from the semi-analytical galaxies in CGs with those obtained from observational data. In addition, we investigated whether the ranking or the luminosity of a galaxy is the most important parameter in the determination of the centre around which the clustering of faint galaxies occurs. Results. There is no particular influence of the extreme compact group (CG) environment on the number of faint galaxies in such groups compared to control groups. When selecting normal groups with separations between the first and second ranked galaxies similar to what is observed in CGs, the faint galaxy projected number density profiles in CGs and normal groups are similar in shape and height. We observed a similar behaviour of the population of faint galaxies in observations and simulations in the regions closer to the first and second ranked galaxies. Finally, we find that the projected density of faint galaxies is higher around luminous galaxies, regardless of the ranking in the CG. Conclusions. The semi-analytical approach shows that CGs and their surroundings do not represent a hostile enough environment to make faint galaxies behave differently than in normal groups.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".