A first-principles FRG approach to two-flavor QCD derives low-energy constants for the pion, sigma-meson and scalar diquark without parameters beyond QCD itself, including new diquark properties for color-superconducting models.
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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,
A physics-informed neural network produces a thermodynamically consistent 4D equation of state for QCD matter that reproduces lattice QCD and hadron resonance gas results while extrapolating to high baryon density for use in hybrid hydrokinetic models.
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Scalar diquarks in the QCD vacuum
A first-principles FRG approach to two-flavor QCD derives low-energy constants for the pion, sigma-meson and scalar diquark without parameters beyond QCD itself, including new diquark properties for color-superconducting models.
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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,
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Equation of State at High Baryon Densities from a Thermodynamically Informed Neural Network
A physics-informed neural network produces a thermodynamically consistent 4D equation of state for QCD matter that reproduces lattice QCD and hadron resonance gas results while extrapolating to high baryon density for use in hybrid hydrokinetic models.