An equation-of-state-meter of quantum chromodynamics transition from deep learning

Long-Gang Pang (), Kai Zhou (), Nan Su (), Hannah Petersen, Horst Stöcker and Xin-Nian Wang
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Long-Gang Pang: Frankfurt Institute for Advanced Studies
Kai Zhou: Frankfurt Institute for Advanced Studies
Nan Su: Frankfurt Institute for Advanced Studies
Hannah Petersen: Frankfurt Institute for Advanced Studies
Horst Stöcker: Frankfurt Institute for Advanced Studies
Xin-Nian Wang: Lawrence Berkeley National Laboratory

Nature Communications, 2018, vol. 9, issue 1, 1-6

Abstract: Abstract A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the 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 quantum chromodynamics. Such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.

Date: 2018
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DOI: 10.1038/s41467-017-02726-3

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