Impact of the uncertainty in α-captures on 22Ne on the weak s-process in massive stars
Bibliographic record
Abstract
Massive stars at solar metallicity contribute to the production of heavy elements with atomic masses between A = 60 and A = 90 via the so-called weak s-process (which takes place during core He and shell C burning phases). Furthermore, recent studies have shown that rotation boosts the s-process production in massive stars at low metallicities, with a production that may reach the barium neutron-magic peak. These results are very sensitive to neutron source and neutron poison reaction rates. For the weak s-process, the main neutron source is the reaction 22Ne(α,n)25Mg, which is in competition with 22Ne(α,γ)26Mg. The uncertainty of both rates strongly affects the nucleosynthesis predictions from stellar model calculations. In this study, we investigate the impact of the uncertainty in α-captures on 22Ne on the s-process nucleosynthesis in massive stars both at solar and at very low metallicity. For this purpose, we post-process, with the Nugrid mppnp code, non-rotating and rotating evolutionary models 25M ⊙ stars at two different metallicities: Z = Z ⊙ and Z = 10 -5 Z ⊙ , respectively. Our results show that uncertainty of 22Ne(α,n)25Mg and 22Ne(α,γ)26Mg rates have a significant impact on the final elemental production especially for metal poor rotating models. Beside uncertainties in the neutron source reactions, for fast rotating massive stars at low metallicity we revisit the impact of the neutron poisoning effect by the reaction chain 16 O(n,γ) 17 O(α, γ)21Ne, in competition with the 17 O(α,n)20Ne, recycling the neutrons captured by 16 O. © 2014 AIP Publishing LLC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".