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We investigate the liquid-gas phase transition of dense matter in supernova explosion by the relativistic mean field approach and fragment based statistical model. The boiling temperature is found to be high (T_{boil} >= 0.7 MeV for rho_B >= 10^{-7} fm^{-3}), and adiabatic paths are shown to go across the boundary of coexisting region even with high entropy. This suggests that materials experienced phase transition can be ejected to outside. We calculated fragment mass and isotope distribution around the boiling point. We found that heavy elements at the iron, the first, second, and third peaks of r-process are abundantly formed at rho_B = 10^{-7}, 10^{-5}, 10^{-3} and 10^{-2} fm^{-3}, respectively.
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
Systems of Bose particles with both repulsive and attractive interactions are studied using the Skyrme-like mean-field model. The phase diagram of such systems exhibits two special lines in the chemical potential-temperature plane: one line which rep
We study an effective relativistic mean-field model of nuclear matter with arbitrary proton fraction at finite temperature in the framework of nonextensive statistical mechanics, characterized by power-law quantum distributions. We investigate the pr
The existence of a liquid-gas phase transition for hot nuclear systems at subsaturation densities is a well established prediction of finite temperature nuclear many-body theory. In this paper, we discuss for the first time the properties of such pha
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