KYDISC: Galaxy Morphology, Quenching, and Mergers in the Cluster Environment
Bibliographic record
Abstract
Abstract We present the KASI-Yonsei Deep Imaging Survey of Clusters targeting 14 clusters at 0.015 ≲ z ≲ 0.144 using the Inamori Magellan Areal Camera and Spectrograph on the 6.5 m Magellan Baade telescope and the MegaCam on the 3.6 m Canada–France–Hawaii Telescope. We provide a catalog of cluster galaxies that lists magnitudes, redshifts, morphologies, bulge-to-total ratios, and local density. Based on the 1409 spectroscopically confirmed cluster galaxies brighter than −19.8 in the r band, we study galaxy morphology, color, and visual features generated by galaxy mergers. We see a clear trend between morphological content and cluster velocity dispersion, which was not presented by previous studies using local clusters. Passive spirals are preferentially found in a highly dense region (i.e., cluster center), indicating that they have gone through environmental quenching. In deep images (μ r′ ∼ 27 ), 20% of our sample shows signatures of recent mergers, which is not expected from theoretical predictions and a low frequency of ongoing mergers in our sample (∼4%). Such a high fraction of recent mergers in the cluster environment supports a scenario that the merger events that made the features have preceded the galaxy accretion into the cluster environment. We conclude that mergers affect a cluster population mainly through the preprocessing of recently accreted galaxies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".