Classify QCD phase transition with deep learning
Year: 2019
Type: article
Abstract: 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.
Source: Nuclear Physics A
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,100
Funders: National Science Foundation, U.S. Department of Energy, National Energy Research Scientific Computing Center, Samson, Deutsche Forschungsgemeinschaft, National Natural Science Foundation of China, China Scholarship Council, Central China Normal University, Lawrence Berkeley National Laboratory
Awards: G1079818582, G1143651532, G122673668, G1286236842, G1571665594, G1677143136, G1816284028, G1863003419, G2857863752, G3083819904, SI2-SSI: Collaborative Research: Jet Energy-Loss Tomography with a Statistically and Computationally Advanced Program Envelope (JETSCAPE), G365575497, G3685081974, G42916606, G5076365615, G5296923526, G6337622905, G654302390, G704498496, G7354628648, G969889393, G983654450
OpenAlex ID: W2911629809