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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".