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Deep learning for quark–gluon plasma detection in the CBM experiment.

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Title: Deep learning for quark–gluon plasma detection in the CBM experiment.
Authors: Sergeev, Fedor1 (AUTHOR) sergeev.fi@phystech.edu, Bratkovskaya, Elena2,3 (AUTHOR) E.Bratkovskaya@gsi.de, Kisel, Ivan2,3,4,5 (AUTHOR) I.Kisel@compeng.uni-frankfurt.de, Vassiliev, Iouri3 (AUTHOR) I.Vassiliev@gsi.de
Source: International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics. 11/30/2020, Vol. 35 Issue 33, pN.PAG-N.PAG. 11p.
Subjects: Convolutional neural networks, Quark-gluon plasma, Heavy ion collisions, Signal convolution, Heavy-ion atom collisions, Deep learning
Abstract: Classification of processes in heavy-ion collisions in the CBM experiment (FAIR/GSI, Darmstadt) using neural networks is investigated. Fully-connected neural networks and a deep convolutional neural network are built to identify quark–gluon plasma simulated within the Parton-Hadron-String Dynamics (PHSD) microscopic off-shell transport approach for central Au+Au collision at a fixed energy. The convolutional neural network outperforms fully-connected networks and reaches 93% accuracy on the validation set, while the remaining only 7% of collisions are incorrectly classified. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 147403121
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PubType: Academic Journal
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  Data: Deep learning for quark–gluon plasma detection in the CBM experiment.
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  Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Quark-gluon+plasma%22">Quark-gluon plasma</searchLink><br /><searchLink fieldCode="DE" term="%22Heavy+ion+collisions%22">Heavy ion collisions</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+convolution%22">Signal convolution</searchLink><br /><searchLink fieldCode="DE" term="%22Heavy-ion+atom+collisions%22">Heavy-ion atom collisions</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
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  Data: Classification of processes in heavy-ion collisions in the CBM experiment (FAIR/GSI, Darmstadt) using neural networks is investigated. Fully-connected neural networks and a deep convolutional neural network are built to identify quark–gluon plasma simulated within the Parton-Hadron-String Dynamics (PHSD) microscopic off-shell transport approach for central Au+Au collision at a fixed energy. The convolutional neural network outperforms fully-connected networks and reaches 93% accuracy on the validation set, while the remaining only 7% of collisions are incorrectly classified. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1142/S0217751X20430022
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      – Code: eng
        Text: English
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        PageCount: 11
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    Subjects:
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Quark-gluon plasma
        Type: general
      – SubjectFull: Heavy ion collisions
        Type: general
      – SubjectFull: Signal convolution
        Type: general
      – SubjectFull: Heavy-ion atom collisions
        Type: general
      – SubjectFull: Deep learning
        Type: general
    Titles:
      – TitleFull: Deep learning for quark–gluon plasma detection in the CBM experiment.
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            NameFull: Sergeev, Fedor
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            NameFull: Bratkovskaya, Elena
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            NameFull: Kisel, Ivan
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            NameFull: Vassiliev, Iouri
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            – D: 30
              M: 11
              Text: 11/30/2020
              Type: published
              Y: 2020
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            – TitleFull: International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics
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