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

Pang, Long-Gang; Zhou, Kai; Su, Nan; Petersen, Hannah; Stocker, Horst; Wang, Xin-Nian

doi:10.1038/s41467-017-02726-3

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

Journal Article · Mon Jan 15 00:00:00 EST 2018 · Nature Communications

DOI:https://doi.org/10.1038/s41467-017-02726-3· OSTI ID:1433129

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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
Frankfurt Inst. for Advanced Studies (FIAS), Frankfurt (Germany); Goethe Univ., Frankfurt (Germany). Inst. fur Theoretische Physik
Frankfurt Inst. for Advanced Studies (FIAS), Frankfurt (Germany)
Frankfurt Inst. for Advanced Studies (FIAS), Frankfurt (Germany); Goethe Univ., Frankfurt (Germany). Inst. fur Theoretische Physik; GSI-Helmholtzzentrum fur Schwerionenforschung, Darmstadt (Germany)
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

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Science Foundation (NSF); National Natural Science Foundation of China (NSFC); Helmholtz Association

Grant/Contract Number:: AC02-05CH11231; ACI-1550228; 11521064; 2014DFG02050; 2015CB856902

OSTI ID:: 1433129

Journal Information:: Nature Communications, Vol. 9, Issue 1; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 81 works

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