The neutron capture process in the He shell in core-collapse suprenovae: Presolar silicon carbide grains as a diagnostic tool for nuclear astrophysics
Bibliographic record
Abstract
Carbon-rich presolar grains are found in primitive meteorites, with isotopic measurements to date suggesting a core-collapse supernovae origin site for some of them.This holds for about 1-2 % of presolar silicon carbide (SiC) grains, so-called Type X and C grains, and about 30 % of presolar graphite grains.Presolar SiC grains of Type X show anomalous isotopic signatures for several elements heavier than iron compared to the solar abundances: most notably for strontium, zirconium, molybdenum, ruthenium and barium.We study the nucleosynthesis of zirconium and molybdenum isotopes in the He-shell of three core-collapse supernovae models of 15, 20 and 25 M ☉ with solar metallicity, and compare the results to measurements of presolar grains.We find the stellar models show a large scatter of isotopic abundances for zirconium and molybdenum, but the mass averaged abundances are qualitatively similar to the measurements.We find all models show an excess of 96 Zr relative to the measurements, but the model abundances are affected by the fractionation between Sr and Zr since a large contribution to 90 Zr is due to the radiogenic decay of 90 Sr.Some supernova models show excesses of 95,97 Mo and depletion of 96 Mo relative to solar.The mass averaged distribution from these models shows an excess of 100 Mo, but this may be alleviated by very recent neutron-capture cross section measurements.We encourage future explorations to assess the impact of the uncertainties in key neutron-capture reaction rates that lie along the n-process path.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".