STRÖMGREN PHOTOMETRY OF GALACTIC GLOBULAR CLUSTERS. II. METALLICITY DISTRIBUTION OF RED GIANTS IN ω CENTAURI
Bibliographic record
Abstract
We present new intermediate-band Strömgren photometry based on more than 300 u , v , b , y images of the Galactic globular cluster ω Cen. Optical data were supplemented with new multiband near-infrared (NIR) photometry (350 J , H , K s images). The final optical–NIR catalog covers a region of more than 20 × 20 arcmin squared across the cluster center. We use different optical–NIR color–color planes together with proper-motion data available in the literature to identify candidate cluster red-giant (RG) stars. By adopting different Strömgren metallicity indices, we estimate the photometric metallicity for ≈4000 RGs, the largest sample ever collected. The metallicity distributions show multiple peaks ([Fe/H] phot = −1.73 ± 0.08, −1.29 ± 0.03, −1.05 ± 0.02, −0.80 ± 0.04, −0.42 ± 0.12, and −0.07 ± 0.08 dex) and a sharp cutoff in the metal-poor (MP) tail ([Fe/H] phot ≲ −2 dex) that agree quite well with spectroscopic measurements. We identify four distinct subpopulations, namely, MP ([Fe/H] ⩽ −1.49), metal-intermediate (MI; −1.49 < [Fe/H] ⩽ −0.93), metal-rich (MR; −0.95 < [Fe/H] ⩽ −0.15), and solar metallicity ([Fe/H] ≈ 0). The last group includes only a small fraction of stars (∼8% ± 5%) and should be confirmed spectroscopically. Moreover, using the difference in metallicity based on different photometric indices, we find that the 19% ± 1% of RGs are candidate CN-strong stars. This fraction agrees quite well with recent spectroscopic estimates and could imply a large fraction of binary stars. The Strömgren metallicity indices display a robust correlation with α-elements ([Ca+Si/H]) when moving from the MI to the MR regime ([Fe/H] ≳ −1.7 dex).
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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 | 0.004 | 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) | 0.001 | 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".