Accuracy of the Photometric Redshifts of Brightest Cluster Galaxies Identified in the CFHTLS-W1
Bibliographic record
Abstract
We determine the accuracy of photometric redshifts for the brightest cluster galaxies (BCGs) identified in the W1 field of the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS). BCGs were identified from the galaxy cluster sample produced by the Wavelet Z Photometric (WaZP) cluster finding algorithm between $0.1 < z < 1$. Provided photometric redshifts with the CFHTLS official galaxy catalogs were compared with spectroscopic redshifts from large surveys. 101713 spectroscopic redshifts have been collected from the databases of major spectroscopic surveys. Cross-matching of 3283 BCGs with this large spectroscopic dataset yielded 1215 BCGs with high-quality spectroscopic redshift. These highly reliable spectroscopic redshifts enabled us to determine the photometric redshift accuracy of BCGs as $\sigma_{NMAD}$=0.020. The outlier fraction is obtained as 1.40\%. The dispersion obtained in this study is significantly better than typical photometric redshift accuracies provided in the CFHTLS releases when all types of galaxies are included, which suggests the use of BCGs as a control object when determining galaxy cluster redshifts.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.001 | 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".