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arXiv · 2010.15043

Nuclear liquid-gas phase transition with machine learning

Rui Wang·Yu-Gang Ma·R. Wada·Lie-Wen Chen·Wan-Bing He·Huan-Ling Liu·Kai-Jia Sun
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Abstract

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 $9.24\pm0.04~\rm MeV$ 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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Rui Wang, Yu-Gang Ma, R. Wada, Lie-Wen Chen, Wan-Bing He, Huan-Ling Liu, Kai-Jia Sun. 2020-10-28. Nuclear liquid-gas phase transition with machine learning. https://doi.org/10.1103/physrevresearch.2.043202

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