Identifying the nature of the QCD transition in heavy-ion collisions with deep learning

  • In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade “after-burner”. As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.

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Author:Yi-Lun DuORCiD, Kai ZhouORCiD, Jan SteinheimerORCiDGND, Long-Gang PangORCiD, Anton MotornenkoORCiDGND, Hong-Shi ZongORCiD, Xin-Nian WangORCiDGND, Horst StöckerORCiDGND
Parent Title (English):Nuclear Physics A
Place of publication:Amsterdam
Document Type:Article
Date of Publication (online):2020/12/10
Date of first Publication:2020/12/10
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Contributing Corporation:International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (28. : 2019 : Wuhan)
Release Date:2023/10/09
Tag:Deep learning; Heavy-ion physics; Hybrid model; QCD equation of state
Article Number:121891
Page Number:4
Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International