SUB-CHANDRASEKHAR WHITE DWARF MERGERS AS THE PROGENITORS OF TYPE Ia SUPERNOVAE
Bibliographic record
Abstract
Type Ia supernovae (SNe Ia) are generally thought to be due to the thermonuclear explosions of carbon–oxygen white dwarfs (CO WDs) with masses near the Chandrasekhar mass. This scenario, however, has two long-standing problems. First, the explosions do not naturally produce the correct mix of elements, but have to be finely tuned to proceed from subsonic deflagration to supersonic detonation. Second, population models and observations give formation rates of near-Chandrasekhar WDs that are far too small. Here, we suggest that SNe Ia instead result from mergers of roughly equal-mass CO WDs, including those that produce sub-Chandrasekhar mass remnants. Numerical studies of such mergers have shown that the remnants consist of rapidly rotating cores that contain most of the mass and are hottest in the center, surrounded by dense, small disks. We argue that the disks accrete quickly, and that the resulting compressional heating likely leads to central carbon ignition. This ignition occurs at densities for which pure detonations lead to events similar to SNe Ia. With this merger scenario, we can understand the type Ia rates and have plausible reasons for the observed range in luminosity and for the bias of more luminous supernovae toward younger populations. We speculate that explosions of WDs slowly brought to the Chandrasekhar limit—which should also occur—are responsible for some of the "atypical" SNe Ia.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".