Optical and near-infrared color distributions of the NGC 4874 globular cluster system
Bibliographic record
Abstract
Abstract Examining both optical and optical-infrared color distributions of the globular cluster (GC) systems in large elliptical galaxies is the key to study how non-linearities in the color-metallicity relations of their GC systems are linked to bimodal optical color distributions. In order to do this for the core of the Coma cluster of galaxies (Abell 1656), centered on the giant elliptical galaxy NGC 4874, we have combined F160W (H160) near-infrared (NIR) imaging data acquired with the Wide Field Camera 3 IR Channel (WFC3/IR), installed on Hubble Space Telescope (HST) in 2009, with F475W (g475) and F814W (I814) optical imaging data from the HST Advanced Camera for Surveys (ACS). Since optical-NIR color distributions of extragalactic GC systems reflect the underlying features of the metallicity distributions, we have probed not only optical g475–I814 and optical-NIR I814–H160 color distributions but also the color-color relation for this GC system. The features of these color distributions have been quantitatively analyzed using the Gaussian Mixture Modeling code. We find that brighter GCs have a much redder mean color than fainter ones. The optical color distribution of the GC system in the Coma cluster core shows the typical bimodality, while the evidence for bimodality is significantly weaker in the optical-NIR color distribution.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".