Speaker
Dr
Kai Zhou
(FIAS, Goethe-University Frankfurt am Main)
Description
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 \rho(p_T,\Phi) provide directly accessible information from experiments. High-level correlations of \rho(p_T,\Phi) 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.
| List of tracks | QCD phase diagram (BES) |
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Primary author
Dr
Kai Zhou
(FIAS, Goethe-University Frankfurt am Main)
Co-authors
Xin-Nian Wang
(Lawrence Berkeley National Lab. (US))
Horst Stoecker
(GSi)
Hannah Petersen
Dr
Long-Gang Pang
(Frankfurt Institute for Advanced Studies, Goethe University)
Dr
Nan Su
(Frankfurt Institute for Advanced Studies)
