THE SENSITIVITY OF CORE-COLLAPSE SUPERNOVAE TO NUCLEAR ELECTRON CAPTURE
Bibliographic record
Abstract
ABSTRACT A weak-rate library aimed at investigating the sensitivity of astrophysical environments to variations of electron-capture rates on medium-heavy nuclei has been developed. With this library, the sensitivity of the core-collapse and early post-bounce phases of core-collapse supernovae to nuclear electron capture is examined. The rates are adjusted by factors consistent with uncertainties indicated by comparing theoretical rates to those deduced from charge-exchange and β -decay measurements. To ensure a model-independent assessment, sensitivity studies across a comprehensive set of progenitors and equations of state are performed. We find a +16/−4% range in the mass of the inner core at shock formation and a ±20% range of peak luminosity during the deleptonization burst. These ranges are five times as large as those seen from a separate progenitor study, where we evaluate the sensitivity of these parameters to 32 presupernova models. Additionally, the simulations are found to be more sensitive to a reduction in electron-capture rates than an enhancement, and specifically to the reduction in rates for neutron-rich nuclei near the N = 50 closed neutron shell. As measurements for medium-heavy ( ) and neutron-rich nuclei are sparse, and because accurate theoretical models that account for nuclear structure considerations of individual nuclei are not readily available, rates for these nuclei may be overestimated. If more accurate estimates confirm this, results from this study indicate that significant changes to the core-collapse trajectory are expected. For this reason, experimental and theoretical efforts should focus on this region of the nuclear chart.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".