报告题目：Machine Learning at BESIII: new developments and challenges
报告人：宋昀轩 博士（瑞士洛桑联邦理工学院）
报告时间：2024年8月2日（周五）下午14:30
报告地点：物理学院新楼五楼D502
报告摘要：
The reconstruction of neutral hadrons, particularly neutrons and antineutrons, presents significant challenges in high-energy physics, especially in facilities like BESIII, where no dedicated hadronic calorimeter is available. This seminar will delve into the intricacies of detecting invisible decay processes in which neutral hadrons play a crucial role, often complicating the observation of such elusive phenomena. Additionally, we will present the latest results from the first observation of the 
process, showcasing the innovative deep learning techniques applied in the identification of neutrons. Furthermore, the seminar will discuss ongoing explorations of (anti-)neutron position and energy reconstruction, highlighting the latest advancements. We will also explore other examples of machine learning in high-energy physics analysis, especially in the study of systematic uncertainties related to machine learning.
报告人简介：
Dr. Yunxuan Song is a scientist affiliated with the École Polytechnique Fédérale de Lausanne(EPFL). He is actively involved in the LHCb and BESIII experiments. His research has focused on charm physics, spectroscopy and new physics, particularly using novel approaches and advanced machine learning techniques. He was honored with BESIII PhD Thesis Award for his outstanding contributions to BESIII in the context of his PhD. thesis.
邀请人：孙亮
