Physical properties of more than one thousand brightest cluster galaxies detected in the Canada France Hawaii Telescope Legacy Survey
Bibliographic record
Abstract
Brightest cluster galaxies (BCGs) are very massive elliptical galaxies found at the centers of clusters. Their study gives clues on the formation and evolution of the clusters in which they are embedded. We analysed here in a homogeneous way the properties of a sample of more than one thousand BCGs in the redshift range 0.15 < z < 0.7, based on images from the Canada France Hawaii Telescope Legacy Survey. Based on the cluster catalogue of 1371 clusters by Sarron et al. (2018), we applied our automatic BCG detection algorithm and identified successfully 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. We found no evolution of the BCG properties with redshift up to z = 0.7, in agreement with previous results by Chu et al. (2021), who analysed an order of magnitude smaller sample, 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. The study of this very large sample of more than one thousand 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 modelisation of the luminosity profiles of BCGs, and give evidence for the presence of an inner structure which can only be resolved on deep surveys with limiting apparent magnitude at 80% completeness m80 > 26 mag/arcsec2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".