The magnetic early B-type Stars II: stellar atmospheric parameters in the era of<i>Gaia</i>
Bibliographic record
Abstract
Atmospheric parameters determined via spectral modelling are unavailable for many of the known magnetic early B-type stars. We utilized high-resolution spectra together with non-local thermodynamic equilibrium models to measure effective temperatures Teff and surface gravities log g of stars for which these measurements are not yet available. We find good agreement between our Teff measurements and previous results obtained both photometrically and spectroscopically. For log g, our results are compatible with previous spectroscopic measurements; however, surface gravities of stars previously determined photometrically have been substantially revised. We furthermore find that log g measurements obtained with HARPSpol are typically about 0.1 dex lower than those from comparable instruments. Luminosities were determined using Gaia Data Release 2 parallaxes. We find Gaia parallaxes to be unreliable for bright stars (V < 6 mag) and for binaries; in these cases we reverted to Hipparcos parallaxes. In general, we find luminosities systematically lower than those previously reported. Comparison of log g and log L to available rotational and magnetic measurements shows no correlation between either parameter with magnetic data, but a clear slow-down in rotation with both decreasing log g and increasing log L, a result compatible with the expectation that magnetic braking should lead to rapid magnetic spin-down that accelerates with increasing mass-loss.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".