On the White Dwarf Cooling Sequence of the Globular Cluster ω Centauri
Bibliographic record
Abstract
We present deep and precise photometry (F435W, F625W, F658N) of ω Cen collected with the Advanced Camera for Surveys (ACS) on board the Hubble Space Telescope ( HST ). We have identified ≈6500 white dwarf (WD) candidates, and the ratio of WD to main-sequence (MS) star counts is found to be at least a factor of 2 larger than the ratio of CO-core WD cooling to MS lifetimes. This discrepancy is not explained by the possible occurrence of a He-enhanced stellar population, since the MS lifetime changes by only 15% when changing from a canonical ( Y = 0.25) to a He-enhanced composition ( Y = 0.42). The presence of some He-core WDs seems able to explain the observed star counts. The fraction of He WDs required ranges from 10% to 80% depending on their mean mass, and it is at least 5 times larger than for field WDs. The comparison in the color-magnitude diagram between theory and observations also supports the presence of He WDs. Empirical evidence indicates that He WDs have been detected in stellar systems hosting a large sample of extreme horizontal branch stars, thus suggesting that a fraction of red giants might avoid the He-core flash.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".