New Experimental <sup>23</sup>Na(α, p)<sup>26</sup>Mg Reaction Rate for Massive Star and Type Ia Supernova Models
Bibliographic record
Abstract
Abstract The 23Na(α, p)26Mg reaction has been identified as having a significant impact on the nucleosynthesis of several nuclei between Ne and Ti in Type Ia supernovae, and of 23Na and 26Al in massive stars. The reaction has been subjected to renewed experimental interest recently, motivated by high uncertainties in early experimental data and in the statistical Hauser-Feshbach models used in reaction rate compilations. Early experiments were affected by target deterioration issues and unquantifiable uncertainties. Three new independent measurements instead are utilizing inverse kinematics and Rutherford scattering monitoring to resolve this. In this work we present directly measured angular distributions of the emitted protons to eliminate a discrepancy in the assumptions made in the recent reaction rate measurements, which results in cross sections differing by a factor of 3. We derive a new combined experimental reaction rate for the 23Na(α, p)26Mg reaction with a total uncertainty of 30% at relevant temperatures. Using our new 23Na(α, p)26Mg rate, the 26Al and 23Na production uncertainty is reduced to within 8%. In comparison, using the factor of 10 uncertainty previously recommended by the rate compilation STARLIB, 26Al and 23Na production was changing by more than a factor of 2. In Type Ia supernova conditions, the impact on production of 23Na is constrained to within 15%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".