A machine learning study to identify spinodal clumping in high energy nuclear collisions

Understand

The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods.

• Coordinate space clumping, which appears in the spinodal decomposition, leaves strong characteristic imprints on the spatial net density distribution in nearly every event which can be detected by modern machine learning techniques.
• On the other hand, the corresponding features in the momentum distributions cannot clearly be detected, by the same machine learning methods, in individual events.
• Only a small subset of events can be systematically differentiated if only the momentum space information is available.