Cosmological Constraints from the Red‐Sequence Cluster Survey
Bibliographic record
Abstract
We present a first cosmological analysis of a refined cluster catalog from the Red-Sequence Cluster Survey (RCS). The input cluster sample is derived from the deepest 72.07 deg 2 of the RCS images, which probe to the highest redshift and lowest mass limits. The catalog contains 956 clusters over 0.35 < z < 0.95, limited by cluster richness and richness error. The calibration of the survey images has been extensively cross-checked against publicly available Sloan Digital Sky Survey imaging, and the cluster redshifts and richness that result from this well-calibrated subset of data are robust. We analyze the cluster sample via a general self-calibration technique. We fit simultaneously for the matter density, Ω m , the normalization of the power spectrum, σ 8 , and four parameters describing the calibration of cluster richness to mass, its evolution with redshift, and scatter in the mass-richness relation. The principal goal of this general analysis is to establish the consistency (or lack thereof) between the fitted parameters (both cosmological and cluster mass observables) and available results on both from independent measures. From an unconstrained analysis, the derived values of Ω m and σ 8 are 0.31 and 0.67 , respectively. An analysis including Gaussian priors on the slope and zero point of the mass-richness relation gives very similar results: 0.30 and 0.70 . Both analyses are in acceptable agreement with the current literature. The derived parameters describing the mass-richness relation in the unconstrained fit are also eminently reasonable and in good agreement with existing follow-up data on both the RCS-1 and other cluster samples. Our results directly demonstrate that future surveys (optical and otherwise), with much larger samples of clusters, can give constraints competitive with other probes of cosmology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".