Close companions to brightest cluster galaxies: support for minor mergers and downsizing
Bibliographic record
Abstract
We identify close companions of brightest cluster galaxies (BCGs) for the purpose of quantifying the rate at which these galaxies grow via mergers. By exploiting deep photometric data from the Canada–France–Hawaii Telescope Legacy Survey (CFHTLS), we probe the number of companions per BCG (Nc) with luminosity ratios down to those corresponding to potential minor mergers of 20:1. We also measure the average luminosity in companions per galaxy (Lc). We find that Nc and Lc rise steeply with luminosity ratio for both the BCGs, and a control sample of other bright, red, cluster galaxies. The trend for BCGs rises more steeply, resulting in a larger number of close companions. For companions within 50 kpc of a BCG, Nc = 1.38 ± 0.14 and Lc = 2.14 ± 0.31 × 1010 L⊙, and for companions within 50 kpc of a luminosity matched control sample of non-BCGs, Nc = 0.87 ± 0.08 and Lc = 1.48 ± 0.20 × 1010 L⊙. This suggests that the BCGs are likely to undergo more mergers compared to otherwise comparable luminous galaxies. Additionally, compared to a local sample of luminous red galaxies, the more distant sample presented in this study (with redshifts between 0.15 and 0.39) shows a higher Nc, suggesting that the younger and smaller BCGs are still undergoing hierarchical formation. Using the Millennium Simulations we model and estimate the level of contamination due to unrelated cluster galaxies. The contamination by interloping galaxies is 50 per cent within projected separations of 50 kpc, but within 30 kpc, 60 per cent of identified companions are real physical companions. We conclude that the luminosity of bound merger candidates down to luminosity ratios of 20:1 could be adding as much as 10 per cent to the mass of a typical BCG over 0.5 Gyr at redshifts of z ∼ 0.3.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".