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Register a new userLiquid-Gas Phase Transition of Supernova Matter and Its Relation to Nucleosynthesis
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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.
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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 body predictions. On the one hand, we test uncertainties of our results arising from changes in the construction of chiral Hamiltonians. We use five different chiral forces with consistently derived three-nucleon interactions. On the other hand, we compare the ladder resummation in the self-consistent Greens functions approach to finite temperature Brueckner--Hartree--Fock calculations. We find that systematics due to Hamiltonians dominate over many-body uncertainties. Based on this wide pool of calculations, we estimate that the critical temperature is $T_c=16 pm 2$ MeV, in reasonable agreement with experimental results. We also find that there is a strong correlation between the critical temperature and the saturation energy in microscopic many-body simulations.
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 represents the first-order liquid-gas phase transition with the critical end point, and another line which represents the onset of Bose-Einstein condensation. The calculations are made for strongly-interacting matter composed of alpha particles. The phase diagram of this matter is qualitatively similar to that observed for the atomic He4 liquid. The sensitivity of the results to the model parameters is studied. For weak interaction coupling the critical point is located at the Bose-condensation line.
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 presence of thermodynamic instability in a warm and asymmetric nuclear medium and study the consequent nuclear liquid-gas phase transition by requiring the Gibbs conditions on the global conservation of baryon number and electric charge fraction. We show that nonextensive statistical effects play a crucial role in the equation of state and in the formation of mixed phase also for small deviations from the standard Boltzmann-Gibbs statistics.
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 phase transition for homogeneous nuclear matter within the Self-Consistent Greens Functions approach. We find a substantial decrease of the critical temperature with respect to the Brueckner-Hartree-Fock approximation. Even within the same approximation, the use of two different realistic nucleon-nucleon interactions gives rise to large differences in the properties of the critical point.
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 learning and classify the liquid and gas phases of nuclei directly from the final state raw experimental data of heavy-ion reactions. Based on a confusion scheme which combines the supervised and unsupervised learning, we obtain the limiting temperature of the nuclear liquid-gas phase transition. Its value $9.24pm0.04~rm MeV$ is consistent with that obtained by the traditional caloric curve method. Our study explores the paradigm of combining the machine-learning techniques with heavy-ion experimental data, and it is also instructive for studying the phase transition of other uncontrollable systems, like QCD matter.
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