The Impact of Updated Reaction Rates on 26Al Production and Destruction in Very Massive Stars
Bibliographic record
Abstract
Al is a key short-lived radionuclide in astrophysics, contributing to galactic γ-ray emission, serving as a tracer of nucleosynthesis in massive stars, and a source of heat in early planetoids.Very Massive Stars (VMSs), stars with masses greater than 100 M ⊙ [1], are potential sources of 26 Al through their enhanced stellar winds.The amount of 26 Al ejected by VMSs depends on the reaction rates of 25 Mg(p,γ) 26 Al, which governs its production, and 26 Al(p,γ) 27 Si, which contributes to its destruction.In this work, updated reaction rates for 25 Mg(p,γ) 26 Al and 26 Al(p,γ) 27 Si were incorporated into the post-processing nucleosynthesis code MPPNP to assess their impact on 26 Al wind yields within VMS models with masses 300 M ⊙ and metallicities of 1 Z ⊙ , 0.1 Z ⊙ , and 0.01 Z ⊙ .Results indicate that the revised rates lead to a general decrease in 26 Al ejection at low metallicity by 28% and 47%, for 0.1 Z ⊙ and 0.01 Z ⊙ respectfully, compared to standard reaction rates, but an increase in ejected 26 Al mass at solar metallicity by 55%.This emphasizes how uncertainties in nuclear reaction rates can significantly affect the predicted yields of 26 Al and other isotopes from VMSs.These rates control production and destruction pathways, their uncertainties can compound through the reaction network, influencing abundances of many isotopes.As such, refining these reaction rates through future experiments and studies will be essential for improving the accuracy and reliability of nucleosynthesis predictions and the models that depend on them.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".