The MiMeS survey of Magnetism in Massive Stars: magnetic analysis of the O-type stars
Bibliographic record
Abstract
We present the analysis performed on spectropolarimetric data of 97 O-type targets included in the framework of the MiMeS (Magnetism in Massive Stars) Survey. Mean Least-Squares Deconvolved Stokes I and V line profiles were extracted for each observation, from which we measured the radial velocity, rotational and non-rotational broadening velocities, and longitudinal magnetic field. The investigation of the Stokes I profiles led to the discovery of 2 new multi-line spectroscopic systems (HD46106, HD204827) and confirmed the presence of a suspected companion in HD37041. We present a modified strategy of the Least-Squares Deconvolution technique aimed at optimising the detection of magnetic signatures while minimising the detection of spurious signatures in Stokes V. Using this analysis, we confirm the detection of a magnetic field in 6 targets previously reported as magnetic by the MiMeS collaboration (HD108, HD47129A2, HD57682, HD148937, CPD-28 2561, and NGC 1624-2), as well as report the presence of signal in Stokes V in 3 new magnetic candidates (HD36486, HD162978, HD199579). Overall, we find a magnetic incidence rate of 7+/-3%, for 108 individual O stars (including all O-type components part of multi-line systems), with a median uncertainty of the longitudinal field measurements of about 50\,G. An inspection of the data reveals no obvious biases affecting the incidence rate or the preference for detecting magnetic signatures in the magnetic stars. Similar to A- and B-type stars, we find no link between the stars' physical properties (e.g. Teff, mass, age) and the presence of a magnetic field. However, the Of?p stars represent a distinct class of magnetic O-type stars.
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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 | 0.002 | 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".