The red-sequence luminosity function in galaxy clusters since z similar to 1
Bibliographic record
Abstract
We use a statistical sample of similar to 500 rich clusters taken from 72 deg(2) of the Red-Sequence Cluster Survey (RCS-1) to study the evolution of similar to 30,000 red-sequence galaxies in clusters over the redshift range 0.35 < z < 0.95. We construct red-sequence luminosity functions (RSLFs) for a well-defined, homogeneously selected, richness-limited sample. The RSLF at higher redshifts shows a deficit of faint red galaxies (to M-V >= -19.7) with their numbers increasing toward the present epoch. This is consistent with the "downsizing" picture in which star formation ended at earlier times for the most massive (luminous) galaxies and more recently for less massive (fainter) galaxies. We observe a richness dependence to the downsizing effect in the sense that, at a given redshift, the drop-off of faint red galaxies is greater for poorer (less massive) clusters, suggesting that star formation ended earlier for galaxies in more massive clusters. The decrease in faint red-sequence galaxies is accompanied by an increase in faint blue galaxies, implying that the process responsible for this evolution of faint galaxies is the termination of star formation, possibly with little or no need for merging. At the bright end, we also see an increase in the number of blue galaxies with increasing redshift, suggesting that termination of star formation in higher mass galaxies may also be an important formation mechanism for higher mass ellipticals. By comparing with a low-redshift Abell cluster sample, we find that the downsizing trend seen within RCS-1 has continued to the local universe.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".