TY  - CONF
A1  - Zhou, Kai
A1  - Pang, Long-Gang
A1  - Su, Nan
A1  - Petersen, Hannah
A1  - Stöcker, Horst
A1  - Wang, Xin-Nian
T1  - Identifying QCD transition using deep learning
T2  - EPJ Web of Conferences
N2  - 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.
Y1  - 2018
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/33577
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-335777
SN  - 2100-014X
VL  - 171
IS  - Article Number 16005
PB  - EDP Sciences
CY  - Les Ulis
ER  -