The Red‐Sequence Cluster Survey. I. The Survey and Cluster Catalogs for Patches RCS 0926+37 and RCS 1327+29
Bibliographic record
Abstract
The Red-Sequence Cluster Survey (RCS) is a ~100 deg 2 , two-filter imaging survey in the R C and z ' filters, designed primarily to locate and characterize galaxy clusters to redshifts as high as z = 1.4. This paper provides a detailed description of the survey strategy and execution, including a thorough discussion of the photometric and astrometric calibration of the survey data. The data are shown to be calibrated to a typical photometric uncertainty of 0.03-0.05 mag, with total astrometric uncertainties less than 0 25 for most objects. We also provide a detailed discussion of the adaptation of a previously described cluster search algorithm (the cluster red-sequence method) to the vagaries of real survey data, with particular attention to techniques for accounting for subtle variations in survey depths caused by changes in seeing and sky brightness and transparency. A first catalog of RCS clusters is also presented, for the survey patches RCS 0926+37 and RCS 1327+29. These catalogs, representing about 10% of the total survey and comprising a total of 429 candidate clusters and groups, contain a total of 67 cluster candidates at a photometric redshift of 0.9 < z < 1.4, down to the chosen significance threshold of 3.29 σ.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.037 |
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".