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

Author

Listed:
  • Long-Gang Pang

    (Frankfurt Institute for Advanced Studies
    University of California
    Lawrence Berkeley National Laboratory)

  • Kai Zhou

    (Frankfurt Institute for Advanced Studies
    Goethe Universität)

  • Nan Su

    (Frankfurt Institute for Advanced Studies)

  • Hannah Petersen

    (Frankfurt Institute for Advanced Studies
    Goethe Universität
    GSI Helmholtzzentrum für Schwerionenforschung)

  • Horst Stöcker

    (Frankfurt Institute for Advanced Studies
    Goethe Universität
    GSI Helmholtzzentrum für Schwerionenforschung)

  • Xin-Nian Wang

    (Lawrence Berkeley National Laboratory
    Central China Normal University)

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.

Suggested Citation

  • Long-Gang Pang & Kai Zhou & Nan Su & Hannah Petersen & Horst Stöcker & Xin-Nian Wang, 2018. "An equation-of-state-meter of quantum chromodynamics transition from deep learning," Nature Communications, Nature, vol. 9(1), pages 1-6, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-017-02726-3
    DOI: 10.1038/s41467-017-02726-3
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    Cited by:

    1. Wang, Lingxiao & Hare, Brian M. & Zhou, Kai & Stöcker, Horst & Scholten, Olaf, 2023. "Identifying lightning structures via machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).

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