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-en...
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Published in | Nature communications Vol. 9; no. 1; pp. 210 - 6 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
15.01.2018
Nature Publishing Group Nature Portfolio |
Subjects | |
Online Access | Get full text |
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Summary: | 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.
The large data generated in heavy-ion collision experiments require careful analysis to understand the physics. Here the authors use the deep-learning method to sort equation of states in QCD transition and analyze the simulated data sets mimicking the heavy-ion collision experiments. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 AC02-05CH11231; ACI-1550228; 11521064; 2014DFG02050; 2015CB856902 Helmholtz Association USDOE Office of Science (SC) National Science Foundation (NSF) National Natural Science Foundation of China (NSFC) |
ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-017-02726-3 |