Time: 10:00 am, May 11, Thursday, 2017  

Location: Room 9409  

Speaker: Dr. Longgang Pang, Lawrence Berkeley National Laboratory  

Title:An EoS-meter of QCD transition from deep learning  

Abstract:Supervised learning with a deep convolutional neural network is used to identify the QCD equation of state (EoS) employed in relativistic hydrodynamic simulations of heavy-ion collisions. The final-state particle spectra ρ(pT,Φ)provide directly accessible information from experiments. High-level correlations of ρ(pT,Φ)learned by the neural network act as an "EoS-meter", effective in detecting the nature of the QCD transition. The EoS-meter is model independent and insensitive to other simulation input, especially the initial conditions. Thus it provides a formidable direct-connection of heavy-ion collision observable with the bulk properties of QCD.