Evolution Of The Cluster Optical Galaxy Luminosity Function In The Cfhtls
Bibliographic record
Abstract
Poster presented at the conference Galaxy evolution Across Time, 12-16 June, Paris, France There is some disagreement in the literature concerning the evolution with redshift of the galaxy luminosity function (GLF) in galaxy clusters. Indeed, it is still unclear whether the red sequence (RS) is enriched by efficient quenching of blue late-type galaxies inside the cluster from z of about 1, or if this RS is built at higher redshift. Solving this contradiction is important to understand the physical processes driving the quenching of galaxies. However, the study of the GLF evolution has been limited to small samples and these different conclusions could be due to cluster to cluster variations. To explore this possibility, we applied our new version of the Adami and MAzure Cluster FInder (AMACFI) to the Canada France Hawaii Telescope Legacy Survey (CFHTLS) W1 field. We thus built a catalogue of thousands of cluster candidates up to z of about 1. We present the selection function of our detection algorithm. We study the evolution of the galaxy luminosity function of galaxy clusters with both redshift and cluster mass, and show preliminary results. Taking advantage of our large sample of clusters, we shall be able to break the degeneracy between redshift and mass dependence in the near future.
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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 | 0.003 | 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 | 0.000 |
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".