Domains and defects in nuclear pasta
Bibliographic record
Abstract
Nuclear pasta topology is an essential ingredient to determine transport properties in the inner crust of neutron stars. We perform semiclassical molecular dynamics simulations of nuclear pasta for proton fractions ${Y}_{p}=0.30$ and ${Y}_{p}=0.40$ near one-third of nuclear saturation density, $n=0.05\phantom{\rule{0.16em}{0ex}}{\mathrm{fm}}^{\ensuremath{-}3}$, at a temperature $T=1.0\phantom{\rule{0.16em}{0ex}}\mathrm{MeV}$. Our simulations are, to our knowledge, the largest nuclear pasta simulations to date and contain up to $3\phantom{\rule{0.16em}{0ex}}276\phantom{\rule{0.16em}{0ex}}800$ nucleons in the ${Y}_{p}=0.30$ and $819\phantom{\rule{0.16em}{0ex}}200$ nucleons in the ${Y}_{p}=0.40$ case. An algorithm to determine which nucleons are part of a given sub-domain in the system is presented. By comparing runs of different sizes we study finite-size effects, equilibration time, the formation of multiple domains and defects in the pasta structures, as well as the structure factor dependence on simulation size. Although we find qualitative agreement between the topological structure and the structure factors of runs with 51 200 nucleons and those with $819\phantom{\rule{0.16em}{0ex}}200$ nucleons or more, we show that simulations with hundreds of thousands of nucleons may be necessary to accurately predict pasta transport properties.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".