Stratification of the elements in the atmospheres of blue horizontal-branch stars
Bibliographic record
Abstract
Blue horizontal-branch (BHB) stars with Teff approximately larger than 11 500 K show several observational anomalies. In globular clusters, they exhibit low rotational velocities, abundance anomalies (as compared to cluster abundances), photometric jumps and gaps and spectroscopic gravities lower than predicted by canonical models. It is commonly believed that the low rotational velocities of these stars permit atomic diffusion to be efficient in their atmosphere thereby causing the observed anomalies. Recent detections of vertical stratification of iron (and some other chemical elements) in several BHB stars concur with this framework. In this paper, improved model atmospheres that include the vertical stratification of the elements are applied to BHB stars to verify if they can explain their observational anomalies. The results from theoretical model atmospheres are consistent with the photometric jumps and gaps observed for BHB stars in globular clusters. It is found that iron stratification in the theoretical models and that obtained from observations have similar tendencies. Our results also show that the spectroscopic gravities obtained while using chemically homogeneous model atmospheres to fit observations are underestimated. These results significantly strengthen the belief that atomic diffusion is responsible for these BHB-star anomalies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".