物理学院 School of Physics, Nanjing University

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An EoS-meter of QCD transition from deep learning

2017年11月22日


报告人:苏楠博士, Frankfurt Institute for Advanced Studies
时 间:11月22日(周三)2 PM
地 点:物理楼233会议室

 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 from the simulated final-state particle spectra ρ(pT,Φ). High-level correlations of ρ(pT,Φ) learned by the neural network act as an effective "EoS-meter" in detecting the nature of the QCD transition. The EoS-meter is model independent and insensitive to other simulation inputs, especially the initial conditions. Thus it provides a powerful direct-connection of heavy-ion collision observables with the bulk properties of QCD.


 

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