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". A...

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Bibliographic Details
Published in:arXiv.org 2020-09
Main Authors: Yi-Lun Du, Zhou, Kai, Steinheimer, Jan, Long-Gang, Pang, Motornenko, Anton, Hong-Shi, Zong, Wang, Xin-Nian, Stoecker, Horst
Format: Article
Language:English
Subjects:
Artificial neural networks
Atomic collisions
Computer simulation
Deep learning
Equations of state
Feature extraction
Heavy ions
Ionic collisions
Neural networks
Pions
Spectra
Transverse momentum
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2009.03059