Impurities and Inhomogeneities in Neutron Matter
Bibliographic record
Abstract
We employ ab-initio calculations to extract important properties of neutron matter, namely the effective mass and static response. In calculating the effective mass, we carry out non-perturbative calculations of the single-particle excitation spectrum in strongly interacting neutron matter. These are microscopic quantum Monte Carlo computations of many-neutron energies at different densities as well as several distinct excited states. As input, we employ both phenomenological and chiral two and three-nucleon interactions. We use the single-particle spectrum to extract the effective mass in neutron matter. With a view to systematizing the error involved in this extraction, we carefully assess the impact of finite-size effects on the quasiparticle dispersion relation. We find an effective-mass ratio that drops from 1 as the density is increased. Employing quantum Monte Carlo with these same interactions, we study response by computing the ground-state energies of neutrons with an external sinusoidal potential at several different densities. We handle finite-size effects via self-consistent energy-density functional (EDF) calculations for 8250 particles in a periodic volume. We combine these QMC and EDF computations in an attempt to produce a model-independent extraction of the static response function. Our results are consistent with the compressibility sum rule, which encapsulates the limiting behavior of the response function starting from the homogeneous equation of state, without using the sum rule as an input constraint. Our predictions on neutron matter can function as benchmarks for other many-body approaches, thereby shedding light on the physics of neutron-star crusts and neutron-rich nuclei.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".