Probing criticality with deep learning in relativistic heavy-ion collisions

Understand

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.