Simulation of energy transport by dark matter scattering in stars
Bibliographic record
Abstract
Abstract Asymmetric dark matter (ADM) that is captured in stars can act as an efficient conductor of heat. Small ADM-induced changes in a star's temperature gradient are known to alter neutrino fluxes and asteroseismological signatures, erase convective cores and modify a star's main sequence lifetime. The Sun's proximity to us makes it an ideal laboratory for studying these effects. However, the two formalisms commonly used to parametrize such heat transport were developed over 30 years ago, and calibrated with a single set of simulations. What's more, both are based on assumptions that break down at the Knudsen transition, where heat transport is maximized. We construct a Monte Carlo simulation to exactly solve the Boltzmann collision equation, determining the steady-state distribution and luminosity carried in stars by ADM with cross sections that depend on velocity and momentum. We find that, although the established (Gould & Raffelt) formalism based on local thermal equilibrium does well for constant cross sections, the isothermal (Spergel & Press) method actually performs better across all models with a simple, universal rescaling function. Based on simulation results, we provide recommendations on the parametrization of DM heat transport in stellar evolution models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".