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The liquid-gas phase transition in hot asymmetric nuclear matter is studied within density-dependent relativistic mean-field models where the density dependence is introduced according to the Brown-Rho scaling and constrained by available data at low densities and empirical properties of nuclear matter. The critical temperature of the liquid-gas phase transition is obtained to be 15.7 MeV in symmetric nuclear matter falling on the lower edge of the small experimental error bars. In hot asymmetric matter, the boundary of the phase-coexistence region is found to be sensitive to the density dependence of the symmetry energy. The critical pressure and the area of phase-coexistence region increases clearly with the softening of the symmetry energy. The critical temperature of hot asymmetric matter separating the gas phase from the LG coexistence phase is found to be higher for the softer symmetry energy.
Neutron star tidal deformability extracted from gravitational wave data provides a novel probe to the interior neutron star structures and the associated nuclear equation of state (EOS). Instead of the popular composition of nucleons and leptons in n
Density dependent parametrization models of the nucleon-meson effective couplings, including the isovector scalar delta-field, are applied to asymmetric nuclear matter. The nuclear equation of state and the neutron star properties are studied in an e
Relativistic mean-field (RMF) models have been widely used in the study of many hadronic frameworks because of several important aspects not always present in nonrelativistic models, such as intrinsic Lorentz covariance, automatic inclusion of spin,
We present first-principle predictions for the liquid-gas phase transition in symmetric nuclear matter employing both two- and three-nucleon chiral interactions. Our discussion focuses on the sources of systematic errors in microscopic quantum many b
The machine-learning techniques have shown their capability for studying phase transitions in condensed matter physics. Here, we employ the machine-learning techniques to study the nuclear liquid-gas phase transition. We adopt an unsupervised learnin