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Record W2051204874 · doi:10.1088/2041-8205/722/2/l157

SUB-CHANDRASEKHAR WHITE DWARF MERGERS AS THE PROGENITORS OF TYPE Ia SUPERNOVAE

2010· article· en· W2051204874 on OpenAlexaff
M. H. van Kerkwijk, Philip Chang, Stephen Justham

Bibliographic record

VenueThe Astrophysical Journal Letters · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGamma-ray bursts and supernovae
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsChandrasekhar limitSupernovaWhite dwarfAstrophysicsPhysicsThermonuclear fusionDeflagrationLuminosityDetonationAstronomyPopulationExplosive materialStarsNuclear physicsChemistryGalaxyPlasma

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.227
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations186
Published2010
Admission routes1
Has abstractyes

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