13-19 May 2018
Venice, Italy
Europe/Zurich timezone
The organisers warmly thank all participants for such a lively QM2018! See you in China in 2019!

An equation-of-state-meter of QCD transition from deep learning with (2+1)-D relativistic viscous hydrodynamics coupled to a hadronic cascade model

15 May 2018, 17:00
2h 40m
First floor and third floor (Palazzo del Casinò)

First floor and third floor

Palazzo del Casinò

Poster Phase diagram and search for the critical point Poster Session

Speaker

Yilun Du (Frankfurt Institute of Advanced Studies, Goethe University Fran)

Description

Supervised learning with a deep convolutional neural network (CNN) is used to identify the QCD equation of state (EoS) employed in event-by-event (2+1)-D relativistic viscous hydrodynamics coupled to a hadronic cascade afterburner" simulations of heavy-ion collisions from the simulated final-state pion spectra $\rho(p_T, \phi)$. High-level correlations of $\rho(p_T,\phi)$ are learned by the neural network, which acts as an effectiveEoS-meter" in distinguishing the nature of the QCD transition. The EoS-meter is robust against many simulation inputs, such as shear viscosity, freeze-out temperature, equilibration time and collision energy. Thus the EoS-meter provides a powerful tool as the direct connection of heavy-ion collision observables with the bulk properties of QCD.

Centralised submission by Collaboration Presenter name already specified
Content type Theory

Primary authors

Yilun Du (Frankfurt Institute of Advanced Studies, Goethe University Fran) Kai Zhou (FIAS, Goethe-University Frankfurt am Main) LongGang Pang (Lawrence Berkeley National Laboratory) Anton Motornenko (Frankfurt Institute for Advanced Studies) Nan Su (Frankfurt Institute for Advanced Studies) Dr Jan Steinheimer Horst Stoecker (GSi) Xin-Nian Wang (Lawrence Berkeley National Lab. (US)) Hong-shi Zong (Nanjing University)

Presentation Materials