Explosive Nucleosynthesis in Core-Collapse Type II Supernovae: Insights from new C, N, Si, and Al-Mg isotopic compositions of presolar grains
Bibliographic record
Abstract
We report C, N, Si, and Al-Mg isotope data for 39 presolar X silicon carbide (SiC) and four silicon nitride grains - a group of presolar grains that condensed in the remnants of core-collapse Type II supernovae (CCSNe) - isolated from the Murchison meteorite. Energy dispersive X-ray (EDX) data were used to determine the Mg and Al contents of the X SiC grains for comparison with the Mg/Al ratios determined by secondary ion mass spectroscopy (SIMS). Previous SIMS studies have used O-rich standards in the absence of alternatives. In this study, the correlated isotopic and elemental data of the X SiC grains enabled accurate determination of the initial 26Al/27Al ratios for the grains. Our new grain data suggest that (i) the literature data for X grains are affected to varying degrees by asteroidal/terrestrial contamination, and (ii) the Al/Mg ratios in SiC are a factor of two (with +/-6% 1 sigma uncertainties) lower than estimated based on the SIMS analyses that used O-rich standards. The lowered Al/Mg ratios result in proportionally higher inferred initial 26Al/27Al ratios for presolar SiC grains. In addition, the suppression of asteroidal/terrestrial contamination in this study leads to the observation of negative trends for 12C/13C-30Si/28Si and 26Al/27Al-30Si/28Si among our CCSN grains. We discuss these isotope trends in the light of explosive CCSN nucleosynthesis models, based on which we provide new insights into several non-traditional CCSN nucleosynthesis processes, including explosive H burning, the existence of a C/Si zone in the outer regions of CCSNe, and neutrino-nucleus reactions in deep CCSN regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".