THE GLOBULAR CLUSTER SYSTEM IN M87: A WIDE-FIELD STUDY WITH CFHT/MEGACAM
Bibliographic record
Abstract
Canada-France-Hawaii Telescope Megacam data in ( g ', r ', i ') are used to obtain deep, wide-field photometry of the globular cluster system (GCS) around M87. A total of 6200 GCs brighter than i ' = 23.0 (roughly equivalent to M I = −8.5) are included in the study, essentially containing almost the entire bright half of the total GC population in the galaxy. The classic bimodal metal-poor and metal-rich sequences of GCs show up clearly. While the spatial distribution of the GCs can be traced detectably outward to R gc ≃ 100 kpc and perhaps further, the blue, metal-poor subpopulation is very much more spatially extended than the red subpopulation. Both the red and blue GC subsystems have radial metallicity gradients, where mean heavy-element abundance scales with a projected galactocentric distance as Z ∼ R −0.12 (blue) and R −0.17 (red). The blue sequence exhibits a strongly significant mass/metallicity relation (MMR) in which the mean metallicity gradually increases with cluster luminosity as Z ∼ L 0.25 ± 0.05 for the luminosity range M I ≲ −10 and the assumption of a constant M / L . However, this relation is also clearly nonlinear: fainter than this level, the sequence is more nearly vertical. This mass/metallicity trend can be understood as the result of self-enrichment within the most massive metal-poor GCs during their formation. The red sequence formally exhibits a negatively sloped MMR, but the numerical solutions and tests show that this red-GC slope is not very significant. In giant elliptical galaxies, the red GCs are likely to represent a broad composite population formed during several major starbursts. If so, the red sequence might display a population-based MMR that could in principle be either positive or negative.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".