Deep learning stochastic processes with QCD phase transition

Understand

It is non-trivial to recognize phase transitions and track dynamics inside a stochastic process because of its intrinsic stochasticity.

• In this paper, we employ the deep learning method to classify the phase orders and predict the damping coefficient of fluctuating systems under Langevin's description.
• As a concrete set-up, we demonstrate this paradigm for the scalar condensation in QCD matter near the critical point, in which the order parameter of chiral phase transition can be characterized in a $1+1$-dimensional Langevin equation for $\sigma$ field.
• In a supervised learning manner, the Convolutional Neural Networks(CNNs) accurately classify the first-order phase transition and crossover based on $\sigma$ field configurations with fluctuations.