Recent arrival of faint cluster galaxies on the red sequence: luminosity functions from 119���deg<sup>2</sup>of CFHTLS
Bibliographic record
Abstract
The global star formation rate has decreased significantly since z∼ 1, for reasons that are not well understood. Red-sequence galaxies, dominating in galaxy clusters, represent the population that have had their star formation shut off, and may therefore be the key to this problem. In this work, we select 127 rich galaxy clusters at 0.17 ≤z≤ 0.36, from 119 deg2 of the Canada–France–Hawaii Telescope Legacy Survey (CFHTLS) optical imaging data, and construct the r′-band red-sequence luminosity functions (LFs). We show that the faint end of the LF is very sensitive to how red-sequence galaxies are selected, and an optimal way to minimize the contamination from the blue cloud is to mirror galaxies on the redder side of the colour–magnitude relation. The LFs of our sample have a significant inflexion centred at , suggesting a mixture of two populations. Combining our survey with low-redshift samples constructed from the Sloan Digital Sky Survey, we show that there is no strong evolution of the faint end of the LF (or the red-sequence dwarf-to-giant ratio) over the redshift range 0.2 ≲z≲ 0.4, but from z∼ 0.2 to ∼0 the relative number of red-sequence dwarf galaxies has increased by a factor of ∼3, implying a significant build-up of the faint end of the cluster red sequence over the last 2.5 Gyr.
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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 | 0.001 | 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) | 0.002 | 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".