A thermodynamically consistent neural-network equation of state for QCD matter at finite temperature and conserved charges that matches known low-density results and extrapolates to high baryon densities for use in relativistic heavy-ion simulations.
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Bayesian analysis finds that the likely ranges of light dark-matter fermion mass and exponential density-profile parameter in hyperon-containing neutron stars are nearly independent of the hadronic model for symmetry-energy slopes between 40 and 58 MeV, with HESS J1731-347 and GW170817 data playing,
citing papers explorer
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Equation of State at High Baryon Densities from a Thermodynamically Informed Neural Network
A thermodynamically consistent neural-network equation of state for QCD matter at finite temperature and conserved charges that matches known low-density results and extrapolates to high baryon densities for use in relativistic heavy-ion simulations.
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Bayesian analysis of density profile of light dark matter elucidating the properties of dark matter admixed neutron stars in the presence of hyperons
Bayesian analysis finds that the likely ranges of light dark-matter fermion mass and exponential density-profile parameter in hyperon-containing neutron stars are nearly independent of the hadronic model for symmetry-energy slopes between 40 and 58 MeV, with HESS J1731-347 and GW170817 data playing,