利用机器学习研究隐粲五夸克态的性质  

Revealing the nature of hidden charm pentaquarks with machine learning

作　　者：张振宇 刘家豪 胡继峰 王倩 Ulf-G.Meißner Zhenyu Zhang;Jiahao Liu;Jifeng Hu;Qian Wang;Ulf-G.Meißner(Guangdong Provincial Key Laboratory of Nuclear Science,Institute of Quantum Matter,South China Normal University,Guangzhou 510006,China;Guangdong-Hong Kong Joint Laboratory of Quantum Matter,Southern Nuclear Science Computing Center,South China Normal University,Guangzhou 510006,China;Helmholtz-Institut für Strahlen-und Kernphysik and Bethe Center for Theoretical Physics,Universität Bonn,Bonn D-53115,Germany;Institute for Advanced Simulation,Institut für Kernphysik and Jülich Center for Hadron Physics,Forschungszentrum Jülich,Jïlich D-52425,Germany;Tbilisi State University,Tbilisi 0186,Georgia)

机构地区：[1]Guangdong Provincial Key Laboratory of Nuclear Science,Institute of Quantum Matter,South China Normal University,Guangzhou 510006,China [2]Guangdong-Hong Kong Joint Laboratory of Quantum Matter,Southern Nuclear Science Computing Center,South China Normal University,Guangzhou 510006,China [3]Helmholtz-Institut für Strahlen-und Kernphysik and Bethe Center for Theoretical Physics,Universität Bonn,Bonn D-53115,Germany [4]Institute for Advanced Simulation,Institut für Kernphysik and Jülich Center for Hadron Physics,Forschungszentrum Jülich,Jïlich D-52425,Germany [5]Tbilisi State University,Tbilisi 0186,Georgia

出　　处：《Science Bulletin》2023年第10期981-989,M0003,共10页科学通报（英文版）

基　　金：partly supported by the National Natural Science Foundation of China(NSFC,12035007);Guangdong Provincial funding(2019QN01X172);Guangdong Major Project of Basic and Applied Basic Research(2020B0301030008);supported by the NSFC(12070131001);the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation,Project-ID 196253076-TRR110)through the funds provided to the Sino-German Collaborative Research Center TRR110"Symmetries and the Emergence of Structure in QCD";supported by the Chinese Academy of Sciences(CAS)President’s International Fellowship Initiative(PIFI)(2018DM0034);Volkswagen Stiftung(93562)。

摘　　要：机器学习已经在生物、医学、生产等多个领域广泛应用并展现了其优势.最近,机器学习在物理领域也开始逐渐兴起并展现出其独特的优点.该文旨在探索机器学习在强子物理中的应用潜力.作者以隐粲五夸克态Pc(4312)、Pc(4440)和Pc(4457)为例,在强子分子态图像下研究它们的性质.研究表明,神经网络方法和基于χ2的拟合方法都能够阐明基于领头阶接触势的基本物理性质.然而,χ2拟合方法无法区分Pc(4440)和Pc(4457)的量子数,神经网络方法则可以.由此,作者进一步分析了每个实验数据点对于神经网络预测的影响,以及在拟合方法中每个数据点对于关键物理参数的重要性.通过对比研究,作者发现神经网络方法可以多维度地利用实验数据信息,在强子物理中具有巨大的应用前景.

关 键 词：Exotic hadrons Machine learning QCD mass spectrum Hidden charm pentaquarks 

分 类 号：O572.33[理学—粒子物理与原子核物理] TP181[理学—物理]