Star Counts in the Globular Cluster Omega Centauri. I. Bright Stellar Components
Bibliographic record
Abstract
We present an extensive photometry on HB, RGB, and MSTO stars in Omega Cen. The central regions of the cluster were covered with a mosaic of F435W, F625W, and F658N-band data collected with ACS/HST. The outer reaches were covered with a large set of U,B,V,I-band data collected with the WFI@2.2m ESO/MPI telescope. The final catalogue includes ~1.7 million stars. We identified ~3,200 likely HB stars and ~12,500 stars brighter than the subgiant branch and fainter than the RGB bumps. The HB morphology changes with the radial distance. The relative number of extreme HB stars decreases from ~30% to ~21% when moving from the center toward the outer regions of the cluster, while the fraction of less hot HB stars increases from ~62% to ~72%. We performed a detailed comparison between observed ratios of different stellar tracers and predictions based on canonical evolutionary models with a primordial helium (Y=0.23) content and metal abundances (Z=0.0002,0.001) that bracket the observed spread in metallicity of Omega Cen stars. We found that the empirical star counts of HB stars are on average larger (30%-40%) than predicted. Moreover, the rate of HB stars is 43% larger than the MSTO rate. The discrepancy between the rate of HB compared with the rate of RG and MSTO stars supports the evidence that we are facing a true excess of HB stars. The same comparison was performed by assuming a mix of stellar populations made with 70% of canonical stars and 30% of He-enhanced stars. The discrepancy between theory and observations decreases by a factor of two when compared with rates predicted by canonical He content models, but still 15%-25% (Y=0.42) and 15%-20% (Y=0.33) higher than observed. Furthermore, the ratio between HB and MSTO star counts are ~24% (Y=0.42) and 30% (Y=0.33) larger than predicted lifetime ratios.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".