The dependence of the evolution of Type Ia SN progenitors on the C-burning rate uncertainty and parameters of convective boundary mixing
Bibliographic record
Abstract
Evolution of a Type Ia supernova (SN Ia) progenitor requires formation of a CO white dwarf (WD), which implies a dependence on the C-burning rate (CBR). It can also be affected by the recently identified possibility of C-flame quenching by convective boundary mixing. We present first results of our study of the combined effect of these two potential sources of uncertainty on the SN Ia progenitor evolution. We consider the possibility that the CBR is higher than its currently recommended value by as much as a factor of 1000 if unidentified resonances are important, or that it is significantly lower because of the hindrance effect. For stellar models that assume the Schwarzschild boundary for convection, the maximum initial mass for the formation of CO WDs increases from Mi ≈ 5.5 M⊙ for the CBR factor of 1000 to Mi ≳ 7.0 M⊙ for the CBR factor of 0.01. For C-flame quenching models, hybrid C–O–Ne WDs form for a range of initial mass of ΔMi ≈ 1 M⊙, which increases a fraction of stars that form WDs capable of igniting C in a thermonuclear runaway. The most extreme case is found for the CBR factor of 0.1 that is supported by the hindrance model. This nuclear physics assumption, combined with C-flame quenching, leads to the formation of a hybrid C–O–Ne WD with a mass of 1.3 M⊙. Such WDs do not need to accrete much mass to reach the Chandrasekhar limit.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".