Topological analysis of scale-invariant spatial fluctuations in ultrarelativistic heavy-ion collisions
This paper presents a novel two-stage topological machine learning framework that combines Topological Data Analysis with deep learning to successfully isolate weak critical fluctuation signals from overwhelming non-critical backgrounds in ultrarelativistic heavy-ion collisions, thereby restoring power-law scaling and enabling the reliable identification of the QCD critical point.