A cluster finding algorithm based on the multiband identification of red sequence galaxies
Bibliographic record
Abstract
We present a new algorithm, CAMIRA, to identify clusters of galaxies in wide-field imaging survey data. We base our algorithm on the stellar population synthesis model to predict colours of red sequence galaxies at a given redshift for an arbitrary set of bandpass filters, with additional calibration using a sample of spectroscopic galaxies to improve the accuracy of the model prediction. We run the algorithm on 11 960 deg 2 of imaging data from the Sloan Digital Sky Survey (SDSS) Data Release 8 to construct a catalogue of 71 743 clusters in the redshift range 0.1 < z < 0.6 with richness after correcting for the incompleteness of the richness estimate greater than 20. We cross-match the cluster catalogue with external cluster catalogues to find that our photometric cluster redshift estimates are accurate with low bias and scatter, and that the corrected richness correlates well with X-ray luminosities and temperatures. We use the publicly available Canada-France-Hawaii Telescope Lensing Survey shear catalogue to calibrate the mass-richness relation from stacked weak lensing analysis. Stacked weak lensing signals are detected significantly for eight subsamples of the SDSS clusters divided by redshift and richness bins, which are then compared with model predictions including miscentring effects to constrain mean halo masses of individual bins. We find the richness correlates well with the halo mass, such that the corrected richness limit of 20 corresponds to the cluster virial mass limit of about 1 10 14 h -1 M for the SDSS DR8 cluster sample.
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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 | 0.001 | 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".