Possible pair-instability supernovae at solar metallicity from magnetic stellar progenitors
Bibliographic record
Abstract
Near-solar metallicity (and low-redshift) pair-instability supernova (PISN) candidates challenge stellar evolution models. Indeed, at such a metallicity, even an initially very massive star generally loses so much mass by stellar winds that it will avoid the electron-positron pair-creation instability. We use recent results showing that a magnetic field at the surface of a massive star can significantly reduce its effective mass-loss rate to compute magnetic models of very massive stars (VMSs) at solar metallicity and explore the possibility that such stars end as PISNe. We implement the quenching of the mass loss produced by a surface dipolar magnetic field into the Geneva stellar evolution code and compute new stellar models with an initial mass of 200 M⊙ at solar metallicity, with and without rotation. This considerably reduces the total amount of mass lost by the star during its life. For the non-rotating model, the total (CO-core) mass of the models is 72.8 M⊙ (70.1 M⊙) at the onset of the electron-positron pair-creation instability. For the rotating model, we obtain 65.6 M⊙ (62.4 M⊙). In both cases, a significant fraction of the internal mass lies in the region where pair instability occurs in the log (T)−log (ρ) plane. The interaction of the reduced mass loss with the magnetic field efficiently brakes the surface of the rotating model, producing a strong shear and hence a very efficient mixing that makes the star evolve nearly homogeneously. The core characteristics of our models indicate that solar metallicity models of magnetic VMSs may evolve to PISNe (and pulsation PISNe).
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".