Unified equations of state for cold nonaccreting neutron stars with Brussels-Montreal functionals. V. Improved parametrization of the nucleon density distributions
Bibliographic record
Abstract
We previously studied the inner crust and the pasta mantle of a neutron star within the fourth-order extended Thomas-Fermi (ETF) approach with consistent proton shell corrections added perturbatively via the Strutinsky integral (SI) theorem together with the contribution due to pairing. To speed up the computations and avoid numerical problems, we adopted parametrized nucleon density distributions. However, the errors incurred by the choice of the parametrization are expected to become more significant as the mean baryon number density is increased, especially in the pasta mantle where the differences in the energy per nucleon of the different phases are very small, typically a few keV. To improve the description of these exotic structures, we discuss the important features that a nuclear profile should fulfill and introduce two new parametrizations. Performing calculations using the BSk24 functional, we find that these parametrizations lead to lower ETF energy solutions for all pasta phases than the parametrization we adopted before and more accurately reproduce the exact equilibrium nucleon density distributions obtained from unconstrained variational calculations. Within the ETFSI method, all parametrizations predict the same composition in the region with quasispherical clusters. However, the two new parametrizations lead to a different mantle structure at mean baryon densities above about $0.07\phantom{\rule{4pt}{0ex}}{\mathrm{fm}}^{\ensuremath{-}3}$, at which point lasagna is energetically favored. Interestingly, spherical clusters reappear in the pasta region. The inverted pasta phases such as bucatini and Swiss cheese are still found in the densest region above the core in all cases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".