Large density fluctuations of conserved charges have been proposed as a promising signature for exploring the QCD critical point in heavy-ion collisions. These fluctuations are expected to exhibit a fractal or scale-invariant behavior, which can be probed by intermittency analysis. Recent high-energy experimental studies reveal that the signal of critical fluctuations related to intermittency is very weak and thus could be easily obscured by the overwhelming background particles in the data sample. Employing a point cloud neural network with topological machine learning, we can successfully classify weak signal events from background noise by the extracted distinct topological features, and accurately determine the intermittency index for weak signal event samples.
Identifying $α$-cluster configurations in $^{20}$Ne via ultracentral Ne+Ne Collisions
Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University（核物理与离子束应用教育部重点实验室，复旦大学现代物理研究所）；Shanghai Research Center for Theoretical Nuclear Physics, NSFC and Fudan University（上海理论核物理中心，国家自然科学基金委员会与复旦大学）
Model Study of Eigen-Microstate Signatures of Criticality in Relativistic Heavy-Ion Collisions
Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University（华中师范大学夸克与轻子物理教育部重点实验室和粒子物理研究所）；College of Physics and Electronic Information Engineering, Guilin University of Technology（桂林理工大学物理与电子信息工程学院）；College of Physics and Technology, Guangxi Normal University（广西师范大学物理科学与技术学院）；School of Physics and Optoelectronic Engineering, Nanjing University of Information Science and Technology（南京信息工程大学物理与光电工程学院）；Department of Physics and Electronic Engineering, Yuncheng University（运城大学物理与电子工程系）；Institute for Advanced Study in Physics, Zhejiang University（浙江大学物理学高等研究中心）；School of Systems Science and Institute of Nonequilibrium Systems, Beijing Normal University（北京师范大学系统科学学院和非平衡系统研究所）
