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Register a new userMultifragmentation and nuclear phase transitions (liquid-fog and liquid-gas)
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Thermal multifragmentation of hot nuclei is interpreted as the nuclear liquid-fog phase transition. The charge distributions of the intermediate mass fragments produced in p(3.6 GeV) + Au and p(8.1 GeV) + Au collisions are analyzed within the statistical multifragmentation model with the critical temperature for the nuclear liquid-gas phase transition Tc as a free parameter. The analysis presented here provides strong support for a value of Tc > 15 MeV.
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Critical temperature Tc for the nuclear liquid-gas phase transition is stimated both from the multifragmentation and fission data. In the first case,the critical temperature is obtained by analysis of the IMF yields in p(8.1 GeV)+Au collisions within the statistical model of multifragmentation (SMM). In the second case, the experimental fission probability for excited 188Os is compared with the calculated one with Tc as a free parameter. It is concluded for both cases that the critical temperature is higher than 16 MeV.
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.
Thermal multifragmentation of hot nuclei is interpreted as the nuclear liquid-fog phase transition inside the spinodal region. The experimental data for p(8.1GeV) + Au collisions are analyzed within the framework of the statistical multifragmentation model (SMM) for the events with emission of at least two IMFs. It is found that the partition of hot nuclei is specified after expansion to a volume equal to Vt = (2.6+-0.3) Vo, with Vo as the volume at normal density. However, the freeze-out volume is found to be twice as large: Vf = (5+-1) Vo.
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.
This review article takes stock of the progress made in understanding the phase transition in hot nuclei and highlights the coherence of observed signatures
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