SEARCHING FOR MAGNETIC FIELDS IN 11 WOLF-RAYET STARS: ANALYSIS OF CIRCULAR POLARIZATION MEASUREMENTS FROM ESPaDOnS
Bibliographic record
Abstract
With recent detections of magnetic fields in some of their progenitor O stars, combined with known strong fields in their possible descendant neutron stars, it is natural to search for magnetic fields in Wolf–Rayet (WR) stars, despite the problems associated with the presence of winds enhanced by an order of magnitude over those of O stars. We continue our search among a sample of 11 bright WR stars following our introductory study in a previous paper of WR6 = EZ CMa using the spectropolarimeter ESPaDOnS at Canada–France–Hawaii Telescope, most of them in all four Stokes parameters. This sample includes six WN stars and five WC stars encompassing a range of spectral subclasses. Six are medium/long-period binaries and three show corotating interaction regions. We report no definite detections of a magnetic field in the winds in which the lines form (which is about the same distance from the center of the star as it is from the surface of the progenitor O star) for any of the eleven stars. Possible reasons and their implications are discussed. Nonetheless, the data show evidence supporting marginal detections for WR134, WR137, and WR138. According to the Bayesian analysis, the most probable field intensities are B wind ∼ 200, 130, and 80 G, respectively, with a 95.4% probability that the magnetic fields present in the observable parts of their stellar wind, if stronger, does not exceed G, ∼1500 G, and ∼1500 G, respectively. In the case of non-detections, we report an average field strength upper limit of 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".