Fast and Slow Crystallization-driven Convection in White Dwarfs
Bibliographic record
Abstract
Abstract We investigate crystallization-driven convection in carbon–oxygen white dwarfs. We present a version of the mixing length theory that self-consistently includes the effects of thermal diffusion and composition gradients, and provides solutions for the convective parameters based on the local heat and composition fluxes. Our formulation smoothly transitions between the regimes of fast adiabatic convection at large Peclet number and slow thermohaline convection at low Peclet number. It also allows for both thermally driven and compositionally driven convection, including correctly accounting for the direction of heat transport for compositionally driven convection in a thermally stable background. We use the MESA stellar evolution code to calculate the composition and heat fluxes during crystallization in different models of cooling white dwarfs, and determine the regime of convection and the convective velocity. We find that convection occurs in the regime of slow thermohaline convection during most of the cooling history of the star. However, at the onset of crystallization, the composition flux is large enough to drive fast overturning convection for a short time (∼10 Myr). We estimate the convective velocities in both of these phases and discuss the implications for explaining observed white dwarf magnetic fields with crystallization-driven dynamos.
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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".