Learning Langevin dynamics with QCD phase transition

  • In this proceeding, the deep Convolutional Neural Networks(CNNs) are deployed to recognize the order of QCD phase transition and predict the dynamical parameters in Langevin processes. To overcome the intrinsic randomness existed in a stochastic process, we treat the final spectra as image-type inputs which preserve sufficient spatiotemporal correlations. As a practical example, 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 σ field. The well-trained CNNs accurately classify the first-order phase transition and crossover from σ field configurations with fluctuations, in which the noise does not impair the performance of the recognition. In reconstructing the dynamics, we demonstrate it is robust to extract the damping coefficients η from the intricate field configurations.

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Author:Lingxiao WangORCiD, Lijia JiangORCiD, Kai ZhouORCiD
Parent Title (English):EPJ Web of Conferences
Publisher:EDP Sciences
Place of publication:Les Ulis
Document Type:Article
Date of Publication (online):2022/02/01
Date of first Publication:2022/02/01
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Contributing Corporation:The 19th International Conference of Strangeness in Quark Matter (SQM 2021)
Release Date:2022/09/28
Page Number:4
Institutes:Physik / Physik
Licence (German):License LogoCreative Commons - Namensnennung 4.0