Probing criticality with deep learning in relativistic heavy-ion collisions

Abstract

Systems with different interactions could develop the same critical behaviour due to the underlying symmetry and universality. Using this principle of universality, we can embed critical correlations modeled on the 3D Ising model into the simulated data of heavy-ion collisions, hiding weak signals of a few inter-particle correlations within a large particle cloud. Employing a point cloud network with dynamical edge convolution, we are able to identify events with critical fluctuations through supervised learning, and pick out a large fraction of signal particles used for decision-making in each single event.

arXiv comment10 pages, 5 figures, version accepted by Physics Letters B

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• Machine learning study to identify collective flow in small and large colliding systems
• Intermittency of charged particles in the hybrid UrQMD+CMC model at energies available at the BNL Relativistic Heavy Ion Collider
• Identifying weak critical fluctuations of intermittency in heavy-ion collisions with topological machine learning
• A deep learning approach for predicting multiple observables in Au+Au collisions at RHIC