Mass Ejection in Failed Supernovae: Equation of State and Neutrino Loss Dependence
Bibliographic record
Abstract
Abstract A failed core-collapse supernova from a nonrotating progenitor can eject mass due to a weakening of gravity associated with neutrino emission from the protoneutron star. This mechanism yields observable transients and sets an upper limit on the mass of the black hole (BH) remnant. Previous global simulations of this mechanism have included neutrino losses parametrically, however, with direct implications for the ejecta mass and energy. Here we evolve the inner supernova core with a spherically symmetric, general-relativistic neutrino radiation-hydrodynamic code until BH formation. We then use the result in a Newtonian code that follows the response of the outer layers of the star to the change in gravity and resolves the surface pressure scale height. We find that the dense-matter equation of state (EOS) can introduce a factor of ∼2 variation in gravitational mass lost to neutrinos, with a stiff EOS matching previous parametric results and a soft EOS yielding lower ejecta masses and energies by a factor of several. This difference is caused primarily by the longer time to BH formation in stiffer EOSs. With a soft EOS, our red and yellow supergiant progenitors fail to unbind mass if hydrogen recombination energy is not included. Using a linear ramp in time for mass-energy lost to neutrinos (with suitable parameters) yields a stellar response within ∼10% of that obtained using the detailed history of neutrino losses. Our results imply quantitative but not qualitative modifications to previous predictions for shock breakout, plateau emission, and final BH masses from these events.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".