A SURVEY OF CHEMICAL SEPARATION IN ACCRETING NEUTRON STARS
Bibliographic record
Abstract
ABSTRACT The heavy element ashes of rp-process hydrogen and helium burning in accreting neutron stars are compressed to high density where they freeze, forming the outer crust of the star. We calculate the chemical separation on freezing for a number of different nuclear mixtures resulting from a range of burning conditions for the rp-process. We confirm the generic result that light nuclei are preferentially retained in the liquid and heavy nuclei in the solid. This is in agreement with the previous study of a 17-component mixture of rp-process ashes by Horowitz et al., but extends that result to a much larger range of compositions. We also find an alternative phase separation regime for the lightest ash mixtures which does not demonstrate this generic behavior. With a few exceptions, we find that chemical separation reduces the expected in the outer crust compared to the initial rp-process ash, where measures the mean-square dispersion in atomic number Z of the nuclei in the mixture. We find that the fractional spread of Z plays a role in setting the amount of chemical separation and is strongly correlated to the divergence between the two/three-component approximations and the full component model. The contrast in Y e between the initial rp-process ashes and the equilibrium liquid composition is similar to that assumed in earlier two-component models of compositionally driven convection, except for very light compositions which produce nearly negligible convective driving. We discuss the implications of these results for observations of accreting neutron 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".