Nuclear liquid-gas phase transition with machine learning

被引：64

作者：

Wang, Rui ^{[1
,2
]}

Ma, Yu-Gang ^{[1
,2
]}

Wada, R. ^{[3
]}

Chen, Lie-Wen ^{[4
,5
]}

He, Wan-Bing ^{[1
]}

Liu, Huan-Ling ^{[2
]}

Sun, Kai-Jia ^{[3
,6
]}

机构：

[1] Fudan Univ, Inst Modern Phys, Key Lab Nucl Phys & Ion Beam Applicat MOE, Shanghai 200433, Peoples R China

[2] Chinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China

[3] Texas A&M Univ, Inst Cyclotron, College Stn, TX 77843 USA

[4] Shanghai Jiao Tong Univ, Sch Phys & Astron, Shanghai 200240, Peoples R China

[5] Shanghai Jiao Tong Univ, Shanghai Key Lab Particle Phys & Cosmol, Shanghai 200240, Peoples R China

[6] Texas A&M Univ, Dept Phys & Astron, College Stn, TX 77843 USA

来源：

PHYSICAL REVIEW RESEARCH | 2020年 / 2卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Gases - Unsupervised learning - Liquefied gases - Phase transitions - Learning algorithms;

D O I：

10.1103/PhysRevResearch.2.043202

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Machine-learning techniques have shown their capability for studying phase transitions in condensed matter physics. Here, we employ 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 +/- 0.04 MeV is consistent with that obtained by the traditional caloric curve method. Our study explores the paradigm of combining machine-learning techniques with heavy-ion experimental data, and it is also instructive for studying the phase transition of other uncontrollable systems, such as QCD matter.

引用

页数：8

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