Model atmospheres of magnetic chemically peculiar stars. A remarkable strong-field Bp SiCrFe star HD137509
Bibliographic record
Abstract
In the last few years we have developed stellar model atmospheres which included effects of anomalous abundances and strong magnetic field. The full treatment of anomalous Zeeman splitting and polarized radiative transfer were introduced in the model atmosphere calculations for the first time. In this investigation we present results of modelling the atmosphere of one of the most extreme magnetic chemically peculiar stars, HD137509. This Bp SiCrFe star has the mean surface magnetic field modulus of about 29kG. We use the recent version of the line-by-line opacity sampling stellar model atmosphere code LLmodels, which incorporates the full treatment of Zeeman splitting of spectral lines, detailed polarized radiative transfer and arbitrary abundances. We compare model predictions with photometric and spectroscopic observations of the star, aiming to reach a self-consistency between the abundance pattern derived from high-resolution spectra and abundances used for model atmosphere calculation. Based on magnetic model atmospheres, we redetermined abundances and fundamental parameters of HD137509 using spectroscopic and photometric observations. This allowed us to obtain a better agreement between observed and theoretical parameters compared to non-magnetic models with individual or scaled-solar abundances. We confirm that the magnetic field effects should be taken into account in the stellar parameter determination and abundance analysis.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".