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Compiled Properties of Nucleonic Matter and Nuclear and Neutron Star Models from Non-Relativistic and Relativistic Interactions

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arxiv 2311.00843 v2 pith:FTHFSIJZ submitted 2023-11-01 nucl-th astro-ph.HE

classification nucl-thastro-ph.HE
keywords neutronmatternuclearpropertiesmodeltheoreticalcomparedforces
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abstract

This paper compiles the model parameters and zero-temperature properties of an extensive collection of published theoretical nuclear interactions, including 255 non-relativistic (Skyrme-like) forces, 270 relativistic mean field (RMF) and point-coupling (RMF-PC) forces, and 13 Gogny-like forces. This forms the most exhaustive tabulation of model parameters to date. The properties of uniform symmetric matter and pure neutron matter at the saturation density are determined. Symmetry properties found from the second-order term of a Taylor expansion in neutron excess are compared to the energy difference of pure neutron and symmetric nuclear matter at the saturation density. Selected liquid-droplet model parameters, including the surface tension and surface symmetry energy, are determined for semi-infinite surfaces. Theoretical liquid droplet model neutron skin thicknesses of the neutron-rich closed-shell nuclei $^{48}$Ca and $^{208}$Pb are compared to published theoretical Hartree-Fock and experimental results. A similar comparison is made between theoretical liquid-droplet and experimental values of dipole polarizabilities. In addition, radii, binding energies, moments of inertia and tidal deformabilities of 1.2, 1.4 and 1.6 M$_\odot$ neutron stars are computed. An extensive correlation analysis of bulk matter, nuclear structure, and low-mass neutron star properties is performed and compared to nuclear experiments and astrophysical observations.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards constraining QCD phase transitions in neutron star interiors: Bayesian Inference with TOV linear response analysis

    nucl-th 2025-01 conditional novelty 6.0 of 10

    A Bayesian framework with analytical TOV linear-response gradients and a neural-network equation of state reconstructs neutron star EoSs and constrains first-order phase transition parameters from simulated mass-radius data.

  2. Exploring the limits of nucleonic metamodelling using different relativistic density functionals

    nucl-th 2025-02 conditional novelty 5.0 of 10

    Comparing two relativistic mean-field model families, the paper shows beta-equilibrium neutron star observations constrain the equation of state but not the proton fraction.

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