Revisiting the rigidly rotating magnetosphere model for σ Ori E – II. Magnetic Doppler imaging, arbitrary field RRM, and light variability★
Bibliographic record
Abstract
The initial success of the Rigidly Rotating Magnetosphere (RRM) model application to the B2Vp star σ Ori E by Townsend, Owocki & Groote triggered a renewed era of observational monitoring of this archetypal object. We utilize high-resolution spectropolarimetry and the magnetic Doppler imaging (MDI) technique to simultaneously determine the magnetic configuration, which is predominately dipolar, with a polar strength Bd = 7.3–7.8 kG and a smaller non-axisymmetric quadrupolar contribution, as well as the surface distribution of abundance of He, Fe, C, and Si. We describe a revised RRM model that now accepts an arbitrary surface magnetic field configuration, with the field topology from the MDI models used as input. The resulting synthetic H α emission and broad-band photometric observations generally agree with observations, however, several features are poorly fit. To explore the possibility of a photospheric contribution to the observed photometric variability, the MDI abundance maps were used to compute a synthetic photospheric light curve to determine the effect of the surface inhomogeneities. Including the computed photospheric brightness modulation fails to improve the agreement between the observed and computed photometry. We conclude that the discrepancies cannot be explained as an effect of inhomogeneous surface abundance. Analysis of the UV light variability shows good agreement between observed variability and computed light curves, supporting the accuracy of the photospheric light variation calculation. We thus conclude that significant additional physics is necessary for the RRM model to acceptably reproduce observations of not only σ Ori E, but also other similar stars with significant stellar wind-magnetic field interactions.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".