Evolution of red-sequence cluster galaxies from redshift 0.8 to 0.4: ages, metallicities, and morphologies
Bibliographic record
Abstract
We present a comprehensive analysis of the stellar population properties and morphologies of red-sequence galaxies in 24 clusters and groups from to . The dataset, consisting of 215 spectra drawn from the ESO Distant Cluster Survey, constitutes the largest spectroscopic sample at these redshifts for which such an analysis has been conducted. Analysis reveals that the evolution of the stellar population properties of red-sequence galaxies depend on their mass: while the properties of the most massive are well described by passive evolution and high-redshift formation, those of the less massive galaxies are consistent with a more extended star-formation history. We show that these scenarios reproduce the index-σ relations and the galaxy colours. The two main results of this work are: (1) the evolution of the line-strength indices for the red-sequence galaxies can be reproduced if 40% of the galaxies with km s-1 entered the red-sequence between to , in agreement with the fraction derived in studies of the luminosity functions; and (2) the percentage of red-sequence galaxies exhibiting early-type morphologies (E and S0) decreases by 20% from to . This can be understood if the red-sequence becomes more populated at later times with disc galaxies whose star formation has been quenched. We conclude that the processes quenching star formation do not necessarily produce a simultaneous morphological transformation of the galaxies entering the red-sequence.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".