Classify QCD phase transition with deep learning

  • The state-of-the-art pattern recognition method in machine learning (deep convolution neural network) is used to identify the equation of state (EoS) employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in QCD. The EoS-meter is model independent and insensitive to other simulation inputs including the initial conditions and shear viscosity for hydrodynamic simulations. Through this study we demonstrate that there is a traceable encoder of the dynamical information from the phase structure that survives the evolution and exists in the final snapshot of heavy ion collisions and one can exclusively and effectively decode these information from the highly complex final output with machine learning when traditional methods fail. Besides the deep neural network, the performance of traditional machine learning classifiers are also provided.

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Author:Long-Gang PangORCiD, Kai ZhouORCiD, Nan SuORCiDGND, Hannah PetersenORCiDGND, Horst StöckerORCiDGND, Xin-Nian WangORCiDGND
Parent Title (English):Nuclear Physics A
Place of publication:Amsterdam
Document Type:Article
Date of Publication (online):2019/01/22
Date of first Publication:2019/01/22
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Contributing Corporation:International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (27. : 2018 : Venedig)
Release Date:2023/10/09
Tag:CLVisc; QCD phase transition; deep learning; heavy ion collision; high energy physics; machine learning
Page Number:4
First Page:867
Last Page:870
Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International