Role of neutron pairing with density-gradient dependence in the semimicroscopic treatment of the inner crust of neutron stars
Bibliographic record
Abstract
Using the fourth-order extended Thomas-Fermi method with Strutinsky-integral shell and pairing corrections, we calculate the inner crust of neutron stars with the BSk31 functional, whose pairing has two terms: (i) a term that is fitted to the results of microscopic calculations on homogeneous nuclear matter (accounting for both medium polarization and self-energy effects) that are more realistic than those of our earlier functionals; (ii) an empirical term that is dependent on the density gradient, which permits an excellent fit to nuclear masses. Both proton and neutron pairing are taken into account, the former in the BCS theory and the latter in the local density approximation. We found that the equilibrium value of the proton number $Z$ remains 40 over the entire density range considered, whether or not neutron pairing is included. The new equation of state and the composition are very similar to those of our previously preferred functional, BSk24. However, the predicted neutron pairing fields are quite different. In particular, clusters are found to be impermeable to the neutron superfluid. The implications for the neutron superfluid dynamics are briefly discussed. Since the new pairing is more realistic, the functional BSk31 is better suited for investigating neutron superfluidity in neutron-star crusts.
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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 | 0.000 |
| 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) | 0.001 | 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".