Bayesian cluster finder: clusters in the CFHTLS Archive Research Survey
Bibliographic record
Abstract
The detection of galaxy clusters in present and future surveys enables measuring mass-to-light ratios, clustering properties, galaxy cluster abundances and, therefore, constraining cosmological parameters. We present a new technique for detecting galaxy clusters, which is based on the matched filter algorithm from a Bayesian point of view. The method is able to determine the position, redshift and richness of the cluster through the maximization of a filter depending on galaxy luminosity, density and photometric redshift combined with a galaxy cluster prior that accounts for colour–magnitude relations and brightest cluster galaxy–redshift relation. We tested the algorithm through realistic mock galaxy catalogues, revealing that the detections are 100 per cent complete and 80 per cent pure for clusters up to z < 1.2 and richer than ΛCL > 20 (Abell richness ∼0, M∼ 4 × 1014 M⊙). The completeness and purity remain approximately the same if we do not include the prior information, implying that this method is able to detect galaxy cluster with and without a well-defined red sequence. We applied the algorithm to the Canada–France–Hawaii Telescope Legacy Survey (CFHTLS) Archive Research Survey data, recovering similar detections as previously published using the same or deeper data plus additional clusters which appear to be real.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".