The impact of (n,γ) reaction rate uncertainties of unstable isotopes on the <i>i</i>-process nucleosynthesis of the elements from Ba to W
Bibliographic record
Abstract
ABSTRACT The abundances of neutron (n)-capture elements in the carbon-enhanced metal-poor (CEMP)-r/s stars agree with predictions of intermediate n-density nucleosynthesis, at Nn ∼ 1013–1015 cm−3, in rapidly accreting white dwarfs (RAWDs). We have performed Monte Carlo simulations of this intermediate-process (i-process) nucleosynthesis to determine the impact of (n,γ) reaction rate uncertainties of 164 unstable isotopes, from 131I to 189Hf, on the predicted abundances of 18 elements from Ba to W. The impact study is based on two representative one-zone models with constant values of Nn = 3.16 × 1014 and 3.16 × 1013 cm−3 and on a multizone model based on a realistic stellar evolution simulation of He-shell convection entraining H in a RAWD model with [Fe/H] = −2.6. For each of the selected elements, we have identified up to two (n,γ) reactions having the strongest correlations between their rate variations constrained by Hauser–Feshbach computations and the predicted abundances, with the Pearson product–moment correlation coefficients |rP| &gt; 0.15. We find that the discrepancies between the predicted and observed abundances of Ba and Pr in the CEMP-i star CS 31062−050 are significantly diminished if the rate of 137Cs(n,γ)138Cs is reduced and the rates of 141Ba(n,γ)142Ba or 141La(n,γ)142La increased. The uncertainties of temperature-dependent β-decay rates of the same unstable isotopes have a negligible effect on the predicted abundances. One-zone Monte Carlo simulations can be used instead of computationally time-consuming multizone Monte Carlo simulations in reaction rate uncertainty studies if they use comparable values of Nn. We discuss the key challenges that RAWD simulations of i process for CEMP-i stars meet by contrasting them with recently published low-Z asymptotic giant branch (AGB) i process.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".