Work

Identifying weak critical fluctuations of intermittency in heavy-ion collisions with topological machine learning

Year: 2025

Type: article

Abstract: 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.

Source: Physics Letters B

DOI: 10.1016/j.physletb.2025.139405

Authors: Rui Wang, Chengrui Qiu, Chuan-Shen Hu, Zhiming Li, Yuanfang Wu

Institutions: Central China Normal University, Nanyang Technological University, Hanjiang Normal University

Language: English

Topic: High-Energy Particle Collisions Research

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: diamond