Mixing Uncertainties in Low-Metallicity AGB Stars: The Impact on Stellar Structure and Nucleosynthesis
Bibliographic record
Abstract
The slow neutron-capture process (s-process) efficiency in low-mass AGB stars (1.5 < M/M⊙ < 3) critically depends on how mixing processes in stellar interiors are handled, which is still affected by considerable uncertainties. In this work, we compute the evolution and nucleosynthesis of low-mass AGB stars at low metallicities using the MESA stellar evolution code. The combined data set includes models with initial masses Mini/M⊙=2 and 3 for initial metallicities Z=0.001 and 0.002. The nucleosynthesis was calculated for all relevant isotopes by post-processing with the NuGrid mppnp code. Using these models, we show the impact of the uncertainties affecting the main mixing processes on heavy element nucleosynthesis, such as convection and mixing at convective boundaries. We finally compare our theoretical predictions with observed surface abundances on low-metallicity stars. We find that mixing at the interface between the He-intershell and the CO-core has a critical impact on the s-process at low metallicities, and its importance is comparable to convective boundary mixing processes under the convective envelope, which determine the formation and size of the 13C-pocket. Additionally, our results indicate that models with very low to no mixing below the He-intershell during thermal pulses, and with a 13C-pocket size of at least ∼3 × 10−4 M⊙, are strongly favored in reproducing observations. Online access to complete yield data tables is also provided.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".