The effects of surface fossil magnetic fields on massive star evolution – II. Implementation of magnetic braking in mesa and implications for the evolution of surface rotation in OB stars
Bibliographic record
Abstract
ABSTRACT The time evolution of angular momentum and surface rotation of massive stars are strongly influenced by fossil magnetic fields via magnetic braking. We present a new module containing a simple, comprehensive implementation of such a field at the surface of a massive star within the Modules for Experiments in Stellar Astrophysics (mesa) software instrument. We test two limiting scenarios for magnetic braking: distributing the angular momentum loss throughout the star in the first case, and restricting the angular momentum loss to a surface reservoir in the second case. We perform a systematic investigation of the rotational evolution using a grid of OB star models with surface magnetic fields (M⋆ = 5–60 M⊙, Ω/Ωcrit = 0.2–1.0, Bp = 1–20 kG). We then employ a representative grid of B-type star models (M⋆ = 5, 10, 15 M⊙, Ω/Ωcrit = 0.2, 0.5, 0.8, Bp = 1, 3, 10, 30 kG) to compare to the results of a recent self-consistent analysis of the sample of known magnetic B-type stars. We infer that magnetic massive stars arrive at the zero-age main sequence (ZAMS) with a range of rotation rates, rather than with one common value. In particular, some stars are required to have close-to-critical rotation at the ZAMS. However, magnetic braking yields surface rotation rates converging to a common low value, making it difficult to infer the initial rotation rates of evolved, slowly rotating 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".