A Search for Chemical Stratification and Magnetic Fields in Eight Field Blue Horizontal-branch Stars<sup>*</sup>
Bibliographic record
Abstract
Abstract In this article, we present the results of an analysis of chemical abundances and a search for magnetic fields in eight field blue horizontal-branch (BHB) stars: BD +01°0548, HD 74721, HD 86986, HD 87112, HD 93329, HD 109995, HD 161817, and HD 167105. This study is based on high-resolution optical spectra obtained with ESPaDOnS at the Canada–France–Hawaii Telescope. We first calculated the average chemical element abundance, the rotational velocity, and the radial velocity of the stars using the ZEEMAN2 spectrum synthesis code with the PHOENIX model atmospheres. We then studied the abundances of titanium and iron inferred from individual lines in the spectra of each star and their variations with their predicted formation depths τ 5000. Similarly to the BHB stars cooler than 11,500 K observed in globular clusters, no clear observational evidence of vertical stratification is detected in the atmosphere of these stars. In the second part of this project, we searched for the presence of a magnetic field in the stars, applying the least-squares deconvolution technique on the Stokes I and V spectra. The measured mean longitudinal magnetic field uncertainties, ranging from 8 to 30 G, effectively rule out the presence of an organized magnetic field in these targets with a strength larger than a few 100 G.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".