Nuclear liquid-gas phase transition with machine learning

doi:10.1103/PhysRevResearch.2.043202

CORC > 上海应用物理研究所 > 中国科学院上海应用物理研究所 > 中科院上海应用物理研究所2011-2017年

	Nuclear liquid-gas phase transition with machine learning
	Wang, Rui 1,4; Ma, Yu-Gang 1,4; Wada, R.5; Chen, Lie-Wen 3; He, Wan-Bing 4; Liu, Huan-Ling 1; Sun, Kai-Jia 2,5
刊名	Physical Review Research
	2020
卷号	2 期号:4
关键词	Learning systems Gases Heavy ions Liquefied gases Unsupervised learning Curve method Final state Heavy ion reactions Limiting temperature Liquid and gas phasis Liquid gas phase transition Machine learning techniques Supervised and unsupervised learning
ISSN号	2643-1564
DOI	10.1103/PhysRevResearch.2.043202
文献子类	期刊论文
英文摘要	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±0.04MeV 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. © 2020 authors.
语种	英语
内容类型	期刊论文
源URL	[http://ir.sinap.ac.cn/handle/331007/33038]
专题	上海应用物理研究所_中科院上海应用物理研究所2011-2017年
作者单位	1.Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai; 201800, China; 2.Department of Physics and Astronomy, Texas AandM University, College Station; TX; 77843, United States 3.School of Physics and Astronomy, Shanghai Key Laboratory for Particle Physics and Cosmology, Shanghai Jiao Tong University, Shanghai; 200240, China; 4.Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai; 200433, China; 5.Cyclotron Institute, Texas AandM University, College Station; TX; 77843, United States;
推荐引用方式 GB/T 7714	Wang, Rui,Ma, Yu-Gang,Wada, R.,et al. Nuclear liquid-gas phase transition with machine learning[J]. Physical Review Research,2020,2(4).
APA	Wang, Rui.,Ma, Yu-Gang.,Wada, R..,Chen, Lie-Wen.,He, Wan-Bing.,...&Sun, Kai-Jia.(2020).Nuclear liquid-gas phase transition with machine learning.Physical Review Research,2(4).
MLA	Wang, Rui,et al."Nuclear liquid-gas phase transition with machine learning".Physical Review Research 2.4(2020).