News from <i>Gaia</i> on <i>σ</i> Ori E: A case study for the wind magnetic braking process
Bibliographic record
Abstract
Context.σ Ori E, a massive helium B-type star, shows high surface rotation and a strong surface magnetic field, potentially challenging the process of wind magnetic braking. Aims. The Gaia satellite provides an accurate distance to σ Ori E and confirms its membership to the σ Ori cluster. We account for these two key pieces of information in order to investigate whether single star models can reproduce the observed properties of σ Ori E and provide new estimates for its metallicity, mass, and age. Methods. We computed rotating stellar models accounting for wind magnetic braking and magnetic quenching of the mass loss. We considered two metallicities (Z = 0.014, with a helium mass fraction Y = 0.273 and Z = 0.020 with Y = 0.266), four initial masses between 8 and 9 M⊙, three initial rotations between 250 and 450 km s−1, and three initial surface equatorial magnetic field between 3 and 7 kG. Differential rotation is assumed for the internal rotation in all models. We looked for models simultaneously accounting for the observed radius, position in the HR diagram, surface velocity, and braking timescale. Results. We obtain that σ Ori E is a very young star (age less than 1 Myr) with an initial mass of around 9 M⊙, a surface equatorial magnetic field of around 7 kG, and a metallicity Z (mass fraction of heavy elements) of around 0.020. No solution is obtained with the present models for a metallicity of Z = 0.014. The initial rotation of the models fitting σ Ori E is not highly constrained and could be anywhere in the range studied here. Because of its very young age, models predict no observable changes of the surface abundances due to rotational mixing. Conclusions. The simultaneous high surface rotation and high surface magnetic field of σ Ori E may simply be a consequence of its young age. This young age implies that the processes responsible for producing the chemical inhomogeneities that are observed at its surface should be rapid. Therefore, for explaining the properties of σ Ori E, there is no necessity to invoke a merging event, although such a scenario cannot be discarded. Other stars (HR 5907, HR 7355, HR 345439, HD 2347, CPD –50°3509) showing similar properties to σ Ori E (fast rotation and strong surface magnetic field) may also be very young stars, although determination of the braking timescales is needed to confirm such a conclusion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".