Compositionally-driven convection in the oceans of accreting neutron stars
Bibliographic record
Abstract
We discuss the effect of chemical separation as matter freezes at the base of the ocean of an accreting neutron star, and argue that the retention of light elements in the liquid acts as a source of buoyancy that drives a slow but continual mixing of the ocean, enriching it substantially in light elements, and leading to a relatively uniform composition with depth. We first consider the timescales associated with different processes that can redistribute elements in the ocean, including convection, sedimentation, crystallization, and diffusion. We then calculate the steady state structure of the ocean of a neutron star for an illustrative model in which the accreted hydrogen and helium burns to produce a mixture of O and Se. Even though the H/He burning produces only 2% oxygen by mass, the steady state ocean has an oxygen abundance more than ten times larger, almost 40% by mass. Furthermore, we show that the convective motions transport heat inwards, with a flux of ~ 0.2 MeV per nucleon for an O-Se ocean, heating the ocean and steepening the outwards temperature gradient. The enrichment of light elements and heating of the ocean due to compositionally-driven convection likely have important implications for carbon ignition models of superbursts.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".