Mass ejection in failed supernovae: variation with stellar progenitor
Bibliographic record
Abstract
We study the ejection of mass during stellar core-collapse when the stalled shock does not revive and a black hole forms. Neutrino emission during the protoneutron star phase causes a decrease in the gravitational mass of the core, resulting in an outward going sound pulse that steepens into a shock as it travels out through the star. We explore the properties of this mass ejection mechanism over a range of stellar progenitors using spherically symmetric, time-dependent hydrodynamic simulations that treat neutrino mass-loss parametrically and follow the shock propagation over the entire star. We find that all types of stellar progenitor can eject mass through this mechanism. The ejected mass is a decreasing function of the surface gravity of the star, ranging from several M⊙ for red supergiants to ∼0.1 M⊙ for blue supergiants and ∼10−3 M⊙ for Wolf–Rayet stars. We find that the final shock energy at the surface is a decreasing function of the core-compactness, and is ≲ 1047–1048 erg in all cases. In progenitors with a sufficiently large envelope, high core-compactness, or a combination of both, the sound pulse fails to unbind mass. Successful mass ejection is accompanied by significant fallback accretion that can last from hours to years. We predict the properties of shock breakout and thermal plateau emission produced by the ejection of the outer envelope of blue supergiant and Wolf–Rayet progenitors in otherwise failed supernovae.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".