Bibliographic Details
| 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] |
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| Database: |
Engineering Source |
