Evolution of the cluster optical galaxy luminosity function in the CFHTLS : breaking the degeneracy between mass and redshift
Bibliographic record
Abstract
Obtaining large samples of galaxy clusters is important for cosmology, since cluster counts as a function of redshift and mass can constrain the parameters of our Universe. They are also useful to understand the formation and evolution of clusters. We develop an improved version of the AMACFI cluster finder (now AMASCFI) and apply it to the 154 deg2 of the Canada France Hawaii Telescope Legacy Survey (CFHTLS) to obtain a catalogue of 1371 cluster candidates with mass M200 > 10^14 Msun and redshift z < 0.7. We derive the selection function of AMASCFI from the Millennium simulation, and cluster masses from a richness-mass scaling relation built from matching our candidates with X-ray detections. We study the evolution of these clusters with mass and redshift by computing the i'-band galaxy luminosity functions (GLFs) for the early (ETGs) and late-type galaxies (LTGs). This sample is 90% pure and 70% complete, therefore our results are representative the cluster population in these redshift and mass ranges. We find an increase of both the ETG and LTG faint populations with decreasing redshift (with Schechter slopes alpha_ETG = -0.65 +/- 0.03 at z=0.6 and alpha_ETG = -0.79 +\- 0.02 at z=0.2) and also a decrease of the LTG bright end, but not of the ETG's. Our large sample allows us to break the degeneracy between mass and redshift, finding that the redshift evolution is more pronounced in high-mass clusters, but that there is no significant dependence of the faint end on mass for a given redshift. These results show that the cluster red sequence is mainly formed at redshift z > 0.7, and that faint ETGs continue to enrich the red sequence through quenching of brighter LTGs at z < 0.7. The efficiency of this quenching is higher in large-mass clusters while the accretion rate of faint LTGs is lower as the more massive clusters have already emptied most of their environment at higher redshifts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".