Identifying QCD transition using deep learning

In this proceeding we review our recent work using supervised learning with a deep convolutional neural network (CNN) to identify the QCD equation of state (EoS) employed in hydrodynamic modeling of heavy-ion collisions 
In this proceeding we review our recent work using supervised learning with a deep convolutional neural network (CNN) to identify the QCD equation of state (EoS) employed in hydrodynamic modeling of heavy-ion collisions given only final-state particle spectra ρ(pT, Ф). We showed that there is a traceable encoder of the dynamical information from phase structure (EoS) that survives the evolution and exists in the final snapshot, which enables the trained CNN to act as an effective “EoS-meter” in detecting the nature of the QCD transition.
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Metadaten
Author:Kai Zhou, Long Gang Pang, Nan Su, Hannah Petersen, Horst Stöcker, Xin Nian Wang
URN:urn:nbn:de:hebis:30:3-335777
DOI:http://dx.doi.org/10.1051/epjconf/201817116005
ISSN:2100-014X
Parent Title (English):EPJ Web of Conferences
Publisher:EDP Sciences
Place of publication:Les Ulis
Document Type:Conference Proceeding
Language:English
Year of Completion:2018
Date of first Publication:2018/02/02
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Contributing Corporation:17th International Conference on Strangeness in Quark Matter (SQM 2017)
Release Date:2019/04/04
Volume:171
Issue:Article Number 16005
Pagenumber:4
HeBIS PPN:448535610
Institutes:Physik
Dewey Decimal Classification:530 Physik
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0

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