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

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. Finally, such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.
Authors:
ORCiD logo [1] ; ORCiD logo [2] ;  [3] ; ORCiD logo [4] ;  [4] ; ORCiD logo [5]
  1. Frankfurt Inst. for Advanced Studies (FIAS), Frankfurt (Germany); Univ. of California, Berkeley, CA (United States). Dept. of Physics; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Nuclear Science Division
  2. Frankfurt Inst. for Advanced Studies (FIAS), Frankfurt (Germany); Goethe Univ., Frankfurt (Germany). Inst. fur Theoretische Physik
  3. Frankfurt Inst. for Advanced Studies (FIAS), Frankfurt (Germany)
  4. Frankfurt Inst. for Advanced Studies (FIAS), Frankfurt (Germany); Goethe Univ., Frankfurt (Germany). Inst. fur Theoretische Physik; GSI-Helmholtzzentrum fur Schwerionenforschung, Darmstadt (Germany)
  5. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Nuclear Science Division; Central China Normal Univ., Wuhan (China). Key Lab. of Quark and Lepton Physics (MOE) and Inst. of Particle Physics
Publication Date:
Grant/Contract Number:
AC02-05CH11231; ACI-1550228; 11521064; 2014DFG02050; 2015CB856902
Type:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 2041-1723
Publisher:
Nature Publishing Group
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC); National Science Foundation (NSF); National Natural Science Foundation of China (NNSFC); Helmholtz Association
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
OSTI Identifier:
1433129

Pang, Long-Gang, Zhou, Kai, Su, Nan, Petersen, Hannah, Stocker, Horst, and Wang, Xin-Nian. An equation-of-state-meter of quantum chromodynamics transition from deep learning. United States: N. p., Web. doi:10.1038/s41467-017-02726-3.
Pang, Long-Gang, Zhou, Kai, Su, Nan, Petersen, Hannah, Stocker, Horst, & Wang, Xin-Nian. An equation-of-state-meter of quantum chromodynamics transition from deep learning. United States. doi:10.1038/s41467-017-02726-3.
Pang, Long-Gang, Zhou, Kai, Su, Nan, Petersen, Hannah, Stocker, Horst, and Wang, Xin-Nian. 2018. "An equation-of-state-meter of quantum chromodynamics transition from deep learning". United States. doi:10.1038/s41467-017-02726-3. https://www.osti.gov/servlets/purl/1433129.
@article{osti_1433129,
title = {An equation-of-state-meter of quantum chromodynamics transition from deep learning},
author = {Pang, Long-Gang and Zhou, Kai and Su, Nan and Petersen, Hannah and Stocker, Horst and Wang, Xin-Nian},
abstractNote = {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. Finally, such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.},
doi = {10.1038/s41467-017-02726-3},
journal = {Nature Communications},
number = 1,
volume = 9,
place = {United States},
year = {2018},
month = {1}
}