The accretion of galaxies into groups and clusters
Bibliographic record
Abstract
We use the galaxy stellar mass and halo merger tree information from the semi-analytic model galaxy catalogue of Font et al. to examine the accretion of galaxies into a large sample of groups and clusters, covering a wide range in halo mass (1012.9 to 1015.3h−1 M⊙), and selected from each of four redshift epochs (z= 0, 0.5, 1.0 and 1.5). We find that clusters at all examined redshifts have accreted a significant fraction of their final galaxy populations through galaxy groups. A 1014.5h−1 M⊙ mass cluster at z= 0 has, on average, accreted ∼40 per cent of its galaxies (Mstellar > 109h−1 M⊙) from haloes with masses greater than 1013h−1 M⊙. Further, the galaxies which are accreted through groups are more massive, on average, than the galaxies accreted through smaller haloes or from the field population. We find that at a given epoch, the fraction of galaxies accreted from isolated environments is independent of the final cluster or group mass. In contrast, we find that observing a cluster of the same halo mass at each redshift epoch implies different accretion rates of isolated galaxies, from 5 to 6 per cent per Gyr at z= 0 to 15 per cent per Gyr at z= 1.5. We find that combining the existence of a Butcher–Oemler effect at z= 0.5 and the observations that galaxies within groups display significant environmental effects with galaxy accretion histories justifies striking conclusions. Namely that the dominant environmental process must begin to occur in haloes of 1012–1013h−1 M⊙, and act over time-scales of >2 Gyr. This argues in favour of a mechanism like ‘strangulation’, in which the hot halo of a galaxy is stripped upon infalling into a more massive halo. This simple model predicts that by z= 1.5 galaxy groups and clusters will display little to no environmental effects. This conclusion may limit the effectiveness of red sequence cluster-finding methods at high redshift.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".