HIGH-RESOLUTION THREE-DIMENSIONAL SIMULATIONS OF CORE-COLLAPSE SUPERNOVAE IN MULTIPLE PROGENITORS
Bibliographic record
Abstract
Three-dimensional (3D) simulations of core-collapse supernovae (CCSNe) are granting new insight into the as-yet-uncertain mechanism that drives successful explosions. While there is still debate about whether explosions are obtained more easily in 3D than in 2D, it is undeniable that there exist qualitative and quantitative differences between the results of 3D and 2D simulations. We present an extensive set of high-resolution 1D, 2D, and 3D CCSN simulations with multispecies neutrino leakage carried out in two different progenitors. Our simulations confirm the results of Couch indicating that 2D explodes more readily than 3D. We argue that this is due to the inadequacies of 2D to accurately capture important aspects of the 3D dynamics. We find that without artificially enhancing the neutrino heating rate, we do not obtain explosions in 3D. We examine the development of neutrino-driven convection and the standing accretion shock instability (SASI) and find that, in separate regimes, either instability can dominate. We find evidence for growth of the SASI for both 15 M ☉ and 27 M ☉ progenitors; however, it is weaker in 3D exploding models. The growth rate of both instabilities is artificially enhanced along the symmetry axis in 2D as compared with our axis-free 3D Cartesian simulations. Our work highlights the growing consensus that CCSNe must be studied in 3D if we hope to solve the mystery of how the explosions are powered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".