Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
Abstract
Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a hadronic cascade “after-burner”. Two different types of equations of state (EoS) of the medium are used in the hydrodynamic evolution. The resulting spectra in transverse momentum and azimuthal angle are used as the input data to train the neural network to distinguish different EoS. Different scenarios for the input data are studied and compared in a systematic way. A clear hierarchy is observed in the prediction accuracy when using the event-by-event, cascade-coarse-grained and event-fine-averaged spectra as input for the network, which are about 80%, 90% and 99%, respectively. A comparison with the prediction performance by deep neural network (DNN) with only the normalized pion transverse momentum spectra is also made. High-level features of pion spectra captured by a carefully-trained neural network were found to be able to distinguish the nature of the QCD transition even in a simulation scenario which is close to the experiments.
- Authors:
-
- Frankfurt Inst. for Advanced Studies (FIAS), Frankfurt (Germany); Goethe Univ., Frankfurt (Germany); Nanjing Univ. (China); Univ. of Bergen (Norway)
- Frankfurt Inst. for Advanced Studies (FIAS), Frankfurt (Germany)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States); Central china Normal Univ., Wuhan (China)
- Frankfurt Inst. for Advanced Studies (FIAS), Frankfurt (Germany); Goethe Univ., Frankfurt (Germany)
- Nanjing Univ. (China); Nanjing Proton Source Research and Design Center (China); Anhui Normal Univ. (China)
- Frankfurt Inst. for Advanced Studies (FIAS), Frankfurt (Germany); Goethe Univ., Frankfurt (Germany); GSI-Darmstadt (Germany)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Nuclear Physics (NP); Trond Mohn Foundation; National Natural Science Foundation of China (NSFC); National Major State Basic Research of China; National Science Foundation (NSF)
- OSTI Identifier:
- 1659711
- Grant/Contract Number:
- AC02-05CH11231; BFS2018REK01; 11475085; 11535005; 11690030; 11221504; 2016Y-FE0129300; 2014CB845404; ACI-1550228
- Resource Type:
- Accepted Manuscript
- Journal Name:
- European Physical Journal. C, Particles and Fields
- Additional Journal Information:
- Journal Volume: 80; Journal Issue: 6; Journal ID: ISSN 1434-6044
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Du, Yi-Lun, Zhou, Kai, Steinheimer, Jan, Pang, Long-Gang, Motornenko, Anton, Zong, Hong-Shi, Wang, Xin-Nian, and Stöcker, Horst. Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning. United States: N. p., 2020.
Web. doi:10.1140/epjc/s10052-020-8030-7.
Du, Yi-Lun, Zhou, Kai, Steinheimer, Jan, Pang, Long-Gang, Motornenko, Anton, Zong, Hong-Shi, Wang, Xin-Nian, & Stöcker, Horst. Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning. United States. https://doi.org/10.1140/epjc/s10052-020-8030-7
Du, Yi-Lun, Zhou, Kai, Steinheimer, Jan, Pang, Long-Gang, Motornenko, Anton, Zong, Hong-Shi, Wang, Xin-Nian, and Stöcker, Horst. Wed .
"Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning". United States. https://doi.org/10.1140/epjc/s10052-020-8030-7. https://www.osti.gov/servlets/purl/1659711.
@article{osti_1659711,
title = {Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning},
author = {Du, Yi-Lun and Zhou, Kai and Steinheimer, Jan and Pang, Long-Gang and Motornenko, Anton and Zong, Hong-Shi and Wang, Xin-Nian and Stöcker, Horst},
abstractNote = {Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a hadronic cascade “after-burner”. Two different types of equations of state (EoS) of the medium are used in the hydrodynamic evolution. The resulting spectra in transverse momentum and azimuthal angle are used as the input data to train the neural network to distinguish different EoS. Different scenarios for the input data are studied and compared in a systematic way. A clear hierarchy is observed in the prediction accuracy when using the event-by-event, cascade-coarse-grained and event-fine-averaged spectra as input for the network, which are about 80%, 90% and 99%, respectively. A comparison with the prediction performance by deep neural network (DNN) with only the normalized pion transverse momentum spectra is also made. High-level features of pion spectra captured by a carefully-trained neural network were found to be able to distinguish the nature of the QCD transition even in a simulation scenario which is close to the experiments.},
doi = {10.1140/epjc/s10052-020-8030-7},
journal = {European Physical Journal. C, Particles and Fields},
number = 6,
volume = 80,
place = {United States},
year = {Wed Jun 10 00:00:00 EDT 2020},
month = {Wed Jun 10 00:00:00 EDT 2020}
}
Web of Science
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