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

## TL;DR

This study uses machine learning to detect spinodal decomposition in heavy ion collisions, finding that coordinate space configurations reveal clear signatures, while momentum space features are less distinguishable.

## Contribution

It demonstrates that modern machine learning techniques can identify spatial signatures of spinodal decomposition in nuclear collisions, highlighting the importance of coordinate space analysis.

## Key findings

- Coordinate space clumping signals are detectable by machine learning.
- Momentum space features are less effective for event classification.
- Skewness peaks at specific beam energies indicating phase transition.

## Abstract

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. This is due to the strong similarity of the two event classes, with and without spinodal decomposition. In such scenarios, conventional event-averaged observables like the baryon number cumulants signal a spinodal non-equilibrium phase transition. Indeed the third-order cumulant, the skewness, does exhibit a peak at the beam energy ($\mathrm{E}_{\mathrm{lab}}= 3-4$ A GeV), where the transient hot and dense system created in the heavy ion collision reaches the first-order phase transition.