3D1D hydro-nucleosynthesis simulations of rapidly accreting white dwarfs and the anomalous abundances of very metal-poor stars
Bibliographic record
Abstract
The most Fe-poor stars are C enhanced. C-enhanced metal-poor (CEMP) stars come in two flavours. CEMP-no stars are only enhanced in C and a few other light elements, while many CEMP stars are enhanced by neutron-capture elements. CEMP stars trace the formation of the first structure and the first stars. Here we present a new scenario to explain the observed abundances of CEMP stars that are enhanced in both Ba and Eu, commonly considered slow and rapid neutron-capture process elements respectively. In rapidly accreting white dwarfs at low Z He-shell flash convection with H ingestion give rise to a hydrodynamic nuclear production environment an intermediate time-scale neutron-capture process, or i process operates. It produces both Ba and Eu, but not some of the bonafide r-process elements such as Ir and Os. We model this process through 3D hydrodynamic convection simulations with 1D multi-zone post-processing. The abundance predictions reproduce the observed abundances of CEMP-r/s star CS31062-050 very well all the way from C to Pb. We thus reclassify this star as a CEMP-i star, which was likely polluted by a rapidly accreting WD. We plan to analyse all other known CEMP-r/s stars in terms of our 3D1D hydro-nucleosynthesis simulations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".