The accelerated build-up of the red sequence in high-redshift galaxy clusters
Bibliographic record
Abstract
We analyse the evolution of the red sequence in a sample of galaxy clusters at redshifts 0.8 < z < 1.5 taken from the HAWK-I Cluster Survey (HCS). The comparison with the low-redshift (0.04 < z < 0.08) sample of the WIde-field Nearby Galaxy-cluster Survey (WINGS) and other literature results shows that the slope and intrinsic scatter of the cluster red sequence have undergone little evolution since z = 1.5. We find that the luminous-to-faint ratio and the slope of the faint end of the luminosity distribution of the HCS red sequence are consistent with those measured in WINGS, implying that there is no deficit of red galaxies at magnitudes fainter than |$M_V^{\ast }$| at high redshifts. We find that the most massive HCS clusters host a population of bright red sequence galaxies at MV < −22.0 mag, which are not observed in low-mass clusters. Interestingly, we also note the presence of a population of very bright (MV < −23.0 mag) and massive (log (M*/M⊙) > 11.5) red sequence galaxies in the WINGS clusters, which do not include only the brightest cluster galaxies and which are not present in the HCS clusters, suggesting that they formed at epochs later than z = 0.8. The comparison with the luminosity distribution of a sample of passive red sequence galaxies drawn from the COSMOS/UltraVISTA field in the photometric redshift range 0.8 < zphot < 1.5 shows that the red sequence in clusters is more developed at the faint end, suggesting that halo mass plays an important role in setting the time-scales for the build-up of 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".