Physical properties of more than one thousand brightest cluster galaxies detected in the Canada-France-Hawaii Telescope Legacy Survey
Bibliographic record
Abstract
Context. Brightest cluster galaxies (BCGs) are very massive elliptical galaxies found at the centres of clusters. Their study gives clues to the formation and evolution of the clusters in which they are embedded. Aims. We analyse here in a homogeneous way the properties of a sample of more than 1000 BCGs in the redshift range 0.15 < z < 0.7, based on images from the Canada-France-Hawaii Telescope Legacy Survey. Methods. Based on a recent catalogue of 1371 clusters, we applied our automatic BCG detection algorithm and successfully identified 70% of the BCGs in our sample. We analysed their 2D photometric properties with GALFIT. We also compared the position angles of the BCG major axes with those of the overall cluster to which they belong. Results. We find no evolution of the BCG properties with redshift up to z = 0.7, in agreement with previous results by Chu et al. (2021, A&A, 649, A42), who analysed a sample an order of magnitude smaller, but reaching a redshift z = 1.8. The Kormendy relation for BCGs is tight and consistent with that of normal elliptical galaxies and BCGs measured by other authors. The position angles of the BCGs and of the cluster to which they belong agree within 30 degrees for 55% of the objects with well-defined position angles. Conclusions. The study of this very large sample of more than 1000 BCGs shows that they were mainly formed before z = 0.7 as we find no significant growth for the luminosities and sizes of central galaxies. We discuss the importance of the intracluster light in the interpretation of these results. We highlight the role of image depth in the modelling of the luminosity profiles of BCGs, and give evidence of the presence of an inner structure which can only be resolved on deep surveys with limiting apparent magnitude at 80% completeness m 80 > 26 mag arcsec −2 .
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".