Bibliographic record
Abstract
Using wide-field BVR c I imaging for a sample of 16 intermediate redshift (0.17 < z < 0.55) galaxy clusters from the Canadian Network for Observational Cosmology Survey, we investigate the dependence of cluster galaxy populations and their evolution on environment. Galaxy photometric redshifts are estimated using an empirical photometric redshift technique and galaxy groups are identified using a modified friends-of-friends algorithm in photometric redshift space. We utilize the red galaxy fraction ( f red ) to infer the evolutionary status of galaxies in clusters, using both individual galaxies and galaxies in groups. We apply the local galaxy density, Σ 5 , derived using the fifth nearest neighbor distance, as a measure of local environment, and the cluster-centric radius, r CL , as a proxy for global cluster environment. Our cluster sample exhibits a Butcher–Oemler effect in both luminosity-selected and stellar-mass-selected samples. We find that f red depends strongly on Σ 5 and r CL , and the Butcher–Oemler effect is observed in all Σ 5 and r CL bins. However, when the cluster galaxies are separated into r CL bins, or into group and nongroup subsamples, the dependence on local galaxy density becomes much weaker. This suggests that the properties of the dark matter halo in which the galaxy resides have a dominant effect on its galaxy population and evolutionary history. We find that our data are consistent with the scenario that cluster galaxies situated in successively richer groups (i.e., more massive dark matter halos) reach a high f red value at earlier redshifts. Associated with this, we observe a clear signature of "preprocessing," in which cluster galaxies belonging to moderately massive infalling galaxy groups show a much stronger evolution in f red than those classified as nongroup galaxies, especially at the outskirts of the cluster. This result suggests that galaxies in groups infalling into clusters are significant contributors to the Butcher–Oemler effect.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".