The Rest-frame <i>H</i>-band Luminosity Function of Red-sequence Galaxies in Clusters at 1.0 < <i>z</i> < 1.3
Bibliographic record
Abstract
We present results on the rest-frame H -band luminosity functions (LFs) of red-sequence galaxies in seven clusters at 1.0 < z < 1.3 from the Gemini Observations of Galaxies in Rich Early Environments Survey. Using deep GMOS and IRAC 3.6 μ m imaging, we identify red-sequence galaxies and measure their LFs down to . By stacking the entire sample, we derive a shallow faint-end slope of and , suggesting that there is a deficit of faint red-sequence galaxies in clusters at high redshift. By comparing the stacked red-sequence LF of our sample with a sample of clusters at z ∼ 0.6, we find an evolution of the faint end of the red sequence over the ∼2.6 Gyr between the two samples, with the mean faint-end red-sequence luminosity growing by more than a factor of 2. The faint-to-luminous ratio of our sample ( ) is consistent with the trend of decreasing ratio with increasing redshift proposed in previous studies. A comparison with the field shows that the faint-to-luminous ratios in clusters are consistent with those in the field at z ∼ 1.15 and exhibit a stronger redshift dependence. Our results support the picture that the buildup of faint red-sequence galaxies occurs gradually over time and suggest that faint cluster galaxies, similar to bright cluster galaxies, already experience the quenching effect induced by the environment at z ∼ 1.15.
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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) | 0.002 | 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".