Characterizing the optical properties of galaxy clusters with GMPhoRCC
Bibliographic record
Abstract
We introduce the Gaussian Mixture full Photometric Red sequence Cluster Characteriser (GMPhoRCC), an algorithm for determining the redshift and richness of a galaxy cluster candidate. By using data from a multiband sky survey with photometric redshifts, a red sequence colour–magnitude relation (CMR) is isolated and modelled and used to characterize the optical properties of the candidate. GMPhoRCC provides significant advantages over existing methods, including treatment of multimodal distributions, variable width full CMR red sequence, richness extrapolation and quality control in order to algorithmically identify catastrophic failures. We present redshift comparisons for clusters from the GMBCG, NORAS, REFLEX and XMM Cluster Survey catalogues, where the GMPhoRCC estimates are in excellent agreement with spectra, showing accurate, unbiased results with low scatter (σδz/(1+z) ∼ 0.017). We conclude with the evaluation of GMPhoRCC performance using empirical Sloan Digital Sky Survey (SDSS) like mock galaxy clusters. GMPhoRCC is shown to produce highly pure characterizations with very low probabilities (<1 per cent) of spurious, clean characterizations. In addition, GMPhoRCC is shown to demonstrate high rates of completeness with respect to recovering redshift, richness and correctly identifying the brightest cluster galaxy (BCG).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".