# Quark Matter 2018

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

### 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.

Content type Theory Presenter name already specified

### 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) Horst Stoecker (GSi) Xin-Nian Wang (Lawrence Berkeley National Lab. (US)) Hong-shi Zong (Nanjing University)