Black-hole excision scheme for general relativistic core-collapse supernova simulations
Bibliographic record
Abstract
Fallback supernovae and the collapsar scenario for long gamma-ray bursts and hypernovae have received considerable interest as pathways to black-hole formation and extreme transient events. Consistent simulations of these scenarios require a general relativistic treatment and need to deal appropriately with the formation of a singularity. Free evolution schemes for the Einstein equations can handle the formation of black holes by means of excision or puncture schemes. However, in constrained schemes, which offer distinct advantages in long-term numerical stability in stellar collapse simulations over well above ${10}^{4}$ light-crossing timescales, the dynamical treatment of black-hole spacetimes is more challenging. Building on previous work on excision in conformally flat spacetimes, we here present the implementation of a black-hole excision scheme for supernova simulations with the coconut-fmt neutrino transport code. We describe in detail a choice of boundary conditions that ensures long-time numerical stability, and also address upgrades to the hydrodynamics solver that are required to stably evolve the relativistic accretion flow onto the black hole. The scheme is currently limited to a spherically symmetric metric, but the hydrodynamics can be treated multidimensionally. For demonstration, we present a spherically symmetric simulation of black-hole formation in an $85{M}_{\ensuremath{\bigodot}}$ star, as well as a two-dimensional simulation of the fallback explosion of the same progenitor. These extend past 9 and 0.3 s after black-hole formation, respectively.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".