GALAXY CLUSTERS AT HIGH REDSHIFT AND EVOLUTION OF BRIGHTEST CLUSTER GALAXIES
Bibliographic record
Abstract
Identification of high-redshift clusters is important for studies of cosmology and cluster evolution. Using photometric redshifts of galaxies, we identify 631 clusters from the Canada–France–Hawaii Telescope (CFHT) wide field, 202 clusters from the CFHT deep field, 187 clusters from the Cosmic Evolution Survey (COSMOS) field, and 737 clusters from the Spitzer Wide-area InfraRed Extragalactic Survey (SWIRE) field. The redshifts of these clusters are in the range 0.1 ≲ z ≲ 1.6. Merging these cluster samples gives 1644 clusters in the four survey fields, of which 1088 are newly identified and more than half are from the large SWIRE field. Among 228 clusters of z ⩾ 1, 191 clusters are newly identified, and most of them from the SWIRE field. With this large sample of high-redshift clusters, we study the color evolution of the brightest cluster galaxies (BCGs). The r ' − z ' and r + − m 3.6 μm colors of the BCGs are consistent with a stellar population synthesis model in which the BCGs are formed at redshift z f ⩾ 2 and evolved passively. The g ' − z ' and B − m 3.6 μm colors of the BCGs at redshifts z > 0.8 are systematically bluer than the passive evolution model for galaxies formed at z f ∼ 2, indicating star formation in high-redshift BCGs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".