Constraining the Redshift Evolution of FIRST Radio Sources in RCS1 Galaxy Clusters
Bibliographic record
Abstract
We conduct a statistical analysis of the radio source population in galaxy clusters as a function of redshift by matching radio sources from the Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) catalog with 618 optically-selected galaxy clusters from the first Red-Sequence Cluster Survey (RCS1). The number of excess radio sources (above the background level) per cluster is 0.14 +/- 0.02 for clusters with 0.35 < z < 0.65 and is 0.10 +/- 0.02 for clusters with 0.65 < z < 0.95. The richest clusters in the sample have more radio sources than clusters with low or intermediate richness. When we divide our sample into bins according to cluster richness, we do not observe any significant difference (> 1.5 sigma) in the number of radio sources per unit of cluster mass for the galaxy clusters with 0.35 < z < 0.65 as compared to the galaxy clusters with 0.65 < z < 0.95. Thus the entire sample can be characterized by the number of (L(1.4 GHz) > 4.1 X 10^(24) W/Hz) radio sources per unit (10^14 solar masses) mass, which we measure to be 0.031 +/- 0.004. We further characterize the population of galaxy cluster-related radio sources through visual inspection of the RCS1 images, finding that although the radio activity of brightest cluster galaxies (BCGs) also does not strongly evolve between our high and low redshift samples, the lower-redshift, richest clusters are more likely to host radio-loud BCGs than the higher-redshift, richest clusters or poorer clusters at the 2-sigma level.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".