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Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning

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成果类型:
期刊论文
作者:
Du, Yi-Lun;Zhou, Kai*;Steinheimer, Jan;Pang, Long-Gang;Motornenko, Anton;...
通讯作者:
Zhou, Kai
作者机构:
[Zhou, Kai; Stoecker, Horst; Motornenko, Anton; Steinheimer, Jan; Du, Yi-Lun] Giersch Sci Ctr, Frankfurt Inst Adv Studies, D-60438 Frankfurt, Germany.
[Stoecker, Horst; Motornenko, Anton; Du, Yi-Lun] Goethe Univ Frankfurt, Inst Theoret Phys, D-60438 Frankfurt, Germany.
[Zong, Hong-Shi; Du, Yi-Lun] Nanjing Univ, Dept Phys, Nanjing 210093, Peoples R China.
[Du, Yi-Lun] Univ Bergen, Dept Phys & Technol, N-5007 Bergen, Norway.
[Wang, Xin-Nian; Pang, Long-Gang] Lawrence Berkeley Natl Lab, Nucl Sci Div, Berkeley, CA 94720 USA.
通讯机构:
[Zhou, Kai] G
Giersch Sci Ctr, Frankfurt Inst Adv Studies, D-60438 Frankfurt, Germany.
语种:
英文
期刊:
EUROPEAN PHYSICAL JOURNAL C
ISSN:
1434-6044
年:
2020
卷:
80
期:
6
页码:
1-17
基金类别:
Y.D. thanks Chun Shen for the helpful illustrations of the usage of iEBE-VISHNU package and Volodymyr Vovchenko for helpful discussions. This work is supported by the Helmholtz Graduate School HIRe for FAIR (Y. D. and A. M.) , by the F&E Programme of GSI Helmholtz Zentrum fr Schwerionenforschung GmbH, Darmstadt (Y. D.), by the Giersch Science Center (Y. D.), by the Walter Greiner Gesellschaft zur Frderung der physikalischen Grundlagenforschung e.V., Frankfurt (Y. D.), by the AI grant of SAMSON AG, Frankfurt (Y. D., K. Z. and J. S.), by the BMBF under the ErUM-Data project (K. Z. and J. S.), by the NVIDIA Corporation with the donation of NVIDIA TITAN Xp GPU for the research (K. Z. and J. S.), and by the Judah M. Eisenberg Laureatus Chair by Goethe University and the Walter Greiner Gesellschaft, Frankfurt (H.St.), by Trond Mohn Foundation under Grant No. BFS2018REK01 (Y. D.), by National Natural Science Foundation of China under Grant Nos. 11475085, 11535005, 11690030 (Y. D. and H. Z.) and 11221504 (X.-N.W.), and National Major state Basic Research and Development of China under Grant Nos. 2016Y-FE0129300 (Y. D. and H. Z.) and 2014CB845404 (X.-N.W.), and the U.S. Department of Energy under Contract Nos. DE-AC02-05CH11231 (L. P. and X.-N.W.), and the U.S. National Science Foundation (NSF) under Grant No. ACI-1550228 (JETSCAPE) (L. P. and X.-N.W.).
机构署名:
本校为其他机构
院系归属:
物理科学与技术学院
摘要:
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...

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