Machine learning transforms the inference of the nuclear equation of state
Yongjia Wang1, Qingfeng Li1,2,3()
1. School of Science, Huzhou University, Huzhou 313000, China 2. Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China 3. School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
Our knowledge of the properties of dense nuclear matter is usually obtained indirectly via nuclear experiments, astrophysical observations, and nuclear theory calculations. Advancing our understanding of the nuclear equation of state (EOS, which is one of the most important properties and of central interest in nuclear physics) has relied on various data produced from experiments and calculations. We review how machine learning is revolutionizing the way we extract EOS from these data, and summarize the challenges and opportunities that come with the use of machine learning.
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