THE COLOR-MAGNITUDE RELATION FOR METAL-POOR GLOBULAR CLUSTERS IN M87: CONFIRMATION FROM DEEP<i>HST</i>/ACS IMAGING
Bibliographic record
Abstract
Metal-poor globular clusters (GCs) are our local link to the earliest epochs of star formation and galaxy building. Studies of extragalactic GC systems using deep, high-quality imaging have revealed a small but significant slope to the color–magnitude relation for metal-poor GCs in a number of galaxies. We present a study of the M87 GC system using deep, archival HST /ACS imaging with the F606W and F814W filters, in which we find a significant color–magnitude relation for the metal-poor GCs. The slope of this relation in the I versus V – I color–magnitude diagram (γ I = −0.024 ± 0.006) is perfectly consistent with expectations based on previously published results using data from the ACS Virgo Cluster Survey. The relation is driven by the most luminous GCs, those with M I ≲ −10, and its significance is largest when fitting metal-poor GCs brighter than M I = −7.8, a luminosity which is ∼1 mag fainter than our fitted Gaussian mean for the luminosity function (LF) of blue, metal-poor GCs (∼0.8 mag fainter than the mean for all GCs). These results indicate that there is a mass scale at which the correlation begins, and is consistent with a scenario where self-enrichment drives a mass–metallicity relationship. We show that previously measured half-light radii of M87 GCs from best-fit PSF-convolved King models are consistent with the more accurate measurements in this study, and we also explain how the color–magnitude relation for metal-poor GCs is real and cannot be an artifact of the photometry. We fit Gaussian and evolved Schechter functions to the luminosity distribution of GCs across all colors, as well as divided into blue and red subpopulations, finding that the blue GCs have a brighter mean luminosity and a narrower distribution than the red GCs. Finally, we present a catalog of astrometry and photometry for 2250 M87 GCs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".