A Merged Catalog of Clusters of Galaxies from Early Sloan Digital Sky Survey Data
Bibliographic record
Abstract
We present a catalog of 799 clusters of galaxies in the redshift range z est = 0.05-0.3 selected from ∼400 deg 2 of early Sloan Digital Sky Survey (SDSS) commissioning data along the celestial equator. The catalog is based on merging two independent selection methods—a color-magnitude red-sequence maxBCG technique (B), and a hybrid matched filter method (H). The BH catalog includes clusters with richness Λ ≥ 40 (matched filter) and N gal ≥ 13 (maxBCG), corresponding to typical velocity dispersion of σ v ≳ 400 km s -1 and mass (within 0.6 h -1 Mpc radius) ≳5 × 10 13 h -1 M ⊙ . This threshold is below Abell richness class 0 clusters. The average space density of these clusters is 2 × 10 -5 h 3 Mpc -3 . All NORAS X-ray clusters and 53 of the 58 Abell clusters in the survey region are detected in the catalog; the five additional Abell clusters are detected below the BH catalog cuts. The cluster richness function is determined and found to exhibit a steeply decreasing cluster abundance with increasing richness. We derive observational scaling relations between cluster richness and observed cluster luminosity and cluster velocity dispersion; these scaling relations provide important physical calibrations for the clusters. The catalog can be used for studies of individual clusters, for comparisons with other sources such as X-ray clusters and active galactic nuclei, and, with proper correction for the relevant selection functions, also for statistical analyses of clusters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".