High-precision magnetic field measurements of Ap and Bp stars
Bibliographic record
Abstract
In this paper we describe a new approach for measuring the mean longitudinal magnetic field and net linear polarization of Ap and Bp stars. As was demonstrated by Wade et al., least-squares deconvolution (LSD; Donati et al.) provides a powerful technique for detecting weak Stokes V, Q and U Zeeman signatures in stellar spectral lines. These signatures have the potential to apply strong new constraints to models of stellar magnetic field structure. Here we point out two important uses of LSD Stokes profiles. First, they can provide very precise determinations of the mean longitudinal magnetic field. In particular, this method allows one frequently to obtain 1σ error bars better than 50 G, and smaller than 20 G in some cases. This method is applicable to both broad- and sharp-lined stars, with both weak and strong magnetic fields, and effectively redefines the quality standard of longitudinal field determinations. Secondly, LSD profiles can in some cases provide a measure of the net linear polarization, a quantity analogous to the broad-band linear polarization recently used to derive detailed magnetic field models for a few stars (e.g. Leroy et al.). In this paper we report new high-precision measurements of the longitudinal fields of 14 magnetic Ap/Bp stars, as well as net linear polarization measurements for four of these stars, derived from LSD profiles.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".