报告摘要：In this talk, I will talk about our recently work "Revealing the nature of hidden charm pentaquarks with machine learning", where we study the nature of the hidden charm pentaquarks, i.e. the Pc(4312), Pc(4440) and Pc(4457), with a neural network approach in pionless effective field theory. In this work, we probe the merit of machine learning in comparison with the normal fitting approach. We find that machine learning can distinguish the two equal solutions of the normal fitting approach in pionless effective field theory. Besides that, we also find that machine learning can directly tell how important each bin is for the interested physics. The similarities and differences between neural network and normal fitting approach demonstrate that neural network can analyze experimental data from different aspect. This study provides more insights about how the neural network-based approach predicts the nature of exotic states from the mass spectrum.
个人简介：王倩，华南师范大学量子物质研究院研究员。2012年博士毕业于中国科学院高能物理研究所。2012年8月至2015年12月，在德国于利希研究中心从事博士后研究工作。2016年1月至2019年在德国波恩大学担任研究助理。2019年入职华南师范大学，获得国家海外高层次人才引进计划青年项目、广东省珠江人才计划青年拔尖项目支持。主要从事奇特强子态理论研究。
ZOOM: 445-645-7666 / 123456