Approach the Gell-Mann–Okubo Formula with Machine Learning
Zhenyu Zhang1,2 , Rui Ma1,2 , Jifeng Hu1,2* , and Qian Wang1,2*
1 Guangdong Provincial Key Laboratory of Nuclear Science, Institute of Quantum Matter, South China Normal University, Guangzhou 510006, China2 Guangdong-Hong Kong Joint Laboratory of Quantum Matter, Southern Nuclear Science Computing Center, South China Normal University, Guangzhou 510006, China
Abstract ：Machine learning is a novel and powerful technology and has been widely used in various science topics. We demonstrate a machine-learning-based approach built by a set of general metrics and rules inspired by physics. Taking advantages of physical constraints, such as dimension identity, symmetry and generalization, we succeed to approach the Gell-Mann–Okubo formula using a technique of symbolic regression. This approach can effectively find explicit solutions among user-defined observables, and can be extensively applied to studying exotic hadron spectrum.
收稿日期: 2022-08-28
出版日期: 2022-10-14
:
12.40.Yx
(Hadron mass models and calculations)
12.39.-x
(Phenomenological quark models)
11.30.-j
(Symmetry and conservation laws)
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2022, Vol. 39 
