A SUPER-EDDINGTON WIND SCENARIO FOR THE PROGENITORS OF TYPE Ia SUPERNOVAE
Bibliographic record
Abstract
The accretion of hydrogen-rich material on to carbon–oxygen white dwarfs (CO WDs) is crucial for understanding Type Ia supernova (SN Ia) from the single-degenerate model, but this process has not been well understood due to the numerical difficulties in treating H and He flashes during the accretion. For CO WD masses from 0.5 to 1.378 M ☉ and accretion rates in the range from 10 −8 to 10 −5 M ☉ yr −1 , we simulated the accretion of solar-composition material on to CO WDs using the state-of-the-art stellar evolution code of MESA . For comparison with steady-state models, we first ignored the contribution from nuclear burning to the luminosity when determining the Eddington accretion rate, and found that the properties of H burning in our accreting CO WD models are similar to those from the steady-state models, except that the critical accretion rates at which the WDs turn into red giants or H-shell flashes occur on their surfaces are slightly higher than those from the steady-state models. However, the super-Eddington wind is triggered at much lower accretion rates than previously thought, when the contribution of nuclear burning to the total luminosity is included. This super-Eddington wind naturally prevents the CO WDs with high accretion rates from becoming red giants, thus presenting an alternative to the optically thick wind proposed by Hachisu et al. Furthermore, the super-Eddington wind works in low-metallicity environments, which may explain SNe Ia observed at high redshifts.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".