Mechanical properties of non-accreting neutron star crusts
Bibliographic record
Abstract
The mechanical properties of a neutron star crust, such as breaking strain and shear modulus, have implications for the detection of gravitational waves from a neutron star as well as bursts from soft Gamma-ray repeaters (SGRs). These properties are calculated here for three different crustal compositions for a non-accreting neutron star that results from three different cooling histories, as well as for a pure iron crust. A simple shear is simulated using molecular dynamics to the crustal compositions by deforming the simulation box. The breaking strain and shear modulus are found to be similar in the four cases, with a breaking strain of ∼0.1 and a shear modulus of ∼1030 dyne cm−2 at a density of ρ = 1014 g cm−3 for simulations with an initially perfect body-centred cubic (BCC) lattice. With these crustal properties and the observed properties of PSR J2124−3358, the predicted strain amplitude of gravitational waves for a maximally deformed crust is found to be greater than the observational upper limits from LIGO. This suggests that the neutron star crust in this case may not be maximally deformed or it may not have a perfect BCC lattice structure. The implications of the calculated crustal properties of bursts from SGRs are also explored. The mechanical properties found for a perfect BCC lattice structure find that crustal events alone cannot be ruled out for triggering the energy in SGR bursts.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".