TY  - JOUR
A1  - Pang, Long-Gang
A1  - Zhou, Kai
A1  - Su, Nan
A1  - Petersen, Hannah
A1  - Stöcker, Horst
A1  - Wang, Xin-Nian
T1  - Classify QCD phase transition with deep learning
T2  - Nuclear Physics A
N2  - 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.
KW  - deep learning
KW  - machine learning
KW  - high energy physics
KW  - heavy ion collision
KW  - QCD phase transition
KW  - CLVisc
Y1  - 2019
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/77562
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-775625
SN  - 0375-9474
VL  - 982
SP  - 867
EP  - 870
PB  - Elsevier
CY  - Amsterdam
ER  -