Modeling He-rich subdwarfs through the hot-flasher scenario
Bibliographic record
Abstract
We present 1D numerical simulations aimed at studying the hot-flasher scenario for the formation of He-rich subdwarf stars. Sequences were calculated for a wide range of metallicities and physical assumptions, such as the stellar mass at the moment of the helium core flash. This allows us to study the two previously proposed flavors of the hot-flasher scenario (“deep” and “shallow” mixing cases) and to identify a third transition type. Our sequences are calculated by solving simultaneously the mixing and burning equations within a diffusive convection picture, and in the context of standard mixing length theory. We are able to follow chemical evolution during deep-mixing events in which hydrogen is burned violently, and therefore able to present a homogeneous set of abundances for different metallicities and varieties of hot-flashers. We extend the scope of our work by analyzing the effects of non-standard assumptions, such as the effect of chemical gradients, extra-mixing at convective boundaries, possible reduction in convective velocities, or the interplay between difussion and mass loss. Particular emphasis is placed on the predicted surface properties of the models. We find that the hot-flasher scenario is a viable explanation for the formation and surface properties of He-sdO stars. Our results also show that, during the early He-core burning stage, element diffusion may produce the transformation of (post hot-flasher) He-rich atmospheres into He-deficient ones. If this is so, then we find that He-sdO stars should be the progenitors of some of the hottest sdB stars.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".