A conservative f(R,T) gravity reformulation decouples the gravitational sector from the microphysical equation of state, enabling computation of neutron star mass-radius relations and tidal deformabilities that satisfy current astrophysical constraints.
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A physics-informed Bayesian neural network learns neutron-star equations of state from theoretical priors and constraints, then generates posterior mass-radius and mass-tidal-deformability distributions consistent with NICER radii and 2-solar-mass limits.
Derives exact expressions for pressure and chemical potentials in the neutron star inner crust within Hartree-Fock and extended Thomas-Fermi frameworks, applicable to catalyzed and accreted matter, with examples using BSk24.
citing papers explorer
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Neutron stars in a conservative $f(R,T)$ gravity
A conservative f(R,T) gravity reformulation decouples the gravitational sector from the microphysical equation of state, enabling computation of neutron star mass-radius relations and tidal deformabilities that satisfy current astrophysical constraints.
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A Physics Informed Bayesian Neural Network for the Neutron Star Equation of State
A physics-informed Bayesian neural network learns neutron-star equations of state from theoretical priors and constraints, then generates posterior mass-radius and mass-tidal-deformability distributions consistent with NICER radii and 2-solar-mass limits.
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Pressure and chemical potentials in the inner crust of a cold neutron star within Hartree-Fock and extended Thomas-Fermi methods
Derives exact expressions for pressure and chemical potentials in the neutron star inner crust within Hartree-Fock and extended Thomas-Fermi frameworks, applicable to catalyzed and accreted matter, with examples using BSk24.