The ACS Virgo Cluster Survey. XIV. Analysis of Color‐Magnitude Relations in Globular Cluster Systems
Bibliographic record
Abstract
We examine the correlation between globular cluster (GC) color and magnitude using HST ACS imaging for a sample of 79 early-type galaxies (-21.7 < M B < -15.2 mag) with accurate SBF distances from the ACS Virgo Cluster Survey. Using the KMM mixture modeling algorithm, we find a highly significant correlation, γ z ≡ d ( g - z )/ dz = -0.037 ± 0.004, between color and magnitude for the subpopulation of blue GCs in the co-added GC color-magnitude diagram of the three brightest Virgo Cluster galaxies (M49, M87, and M60): brighter GCs are redder than their fainter counterparts. For the single GC systems of M87 and M60, we find similar correlations; M49 does not appear to show a significant trend. There is no correlation between ( g - z ) and M z for GCs of the red subpopulation. The correlation γ g ≡ d ( g - z )/ dg for the blue subpopulation is much weaker than d ( g - z )/ dz . Using Monte Carlo simulations, we attribute this finding to the fact that the blue subpopulation in M g extends to higher luminosities than does the red subpopulation, which biases the KMM fit results. The correlation between color and M z thus is a real effect: this conclusion is supported by biweight fits to the same color distributions. We identify two environmental dependencies that influence the derived color-magnitude relation: (1) the slope decreases in significance with decreasing galaxy luminosity; and (2) the slope is stronger for GC populations located at smaller galactocentric distances. We examine several physical mechanisms that might give rise to the observed color-magnitude relation: (1) presence of contaminators; (2) accretion of GCs from low-mass galaxies; (3) stochastic effects; (4) the capture of field stars by individual GCs; and (5) GC self-enrichment. We conclude that self-enrichment and field-star capture, or a combination of these processes, offer the most promising means of explaining our observations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.042 | 0.029 |
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".