Neural network study of the nuclear ground-state spin distribution within a random interaction ensemble

作     者：Deng Liu Alam Noor A Zhen-Zhen Qin Yang Lei Deng Liu;Alam Noor A;Zhen-Zhen Qin;Yang Lei

作者机构：School of Mathematics and PhysicsSouthwest University of Science and TechnologyMianyang 621010China School of Nuclear Science and TechnologySouthwest University of Science and TechnologyMianyang 621010China 

出 版 物：《Nuclear Science and Techniques》 (核技术（英文）)

年 卷 期：2024年第35卷第3期

页      面：216-227页

核心收录：

学科分类：08[工学] 081104[工学-模式识别与智能系统] 0827[工学-核科学与技术] 0811[工学-控制科学与工程] 0702[理学-物理学] 

基　　金：supported by the National Natural Science Foundation of China Youth Fund(12105234)。 

主　　题：Neural network Two-body random ensemble Spin distribution of nuclear ground state 

摘      要：The distribution of the nuclear ground-state spin in a two-body random ensemble(TBRE)was studied using a general classification neural network(NN)model with two-body interaction matrix elements as input features and the corresponding ground-state spins as labels or output predictions.The quantum many-body system problem exceeds the capability of our optimized NNs in terms of accurately predicting the ground-state spin of each sample within the TBRE.However,our NN model effectively captured the statistical properties of the ground-state spin because it learned the empirical regularity of the ground-state spin distribution in TBRE,as discovered by physicists.