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An EoS-meter of QCD transition from deep learning

Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stocker, Xin-Nian Wang
Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany
arXiv:1612.04262 [hep-ph], (13 Dec 2016)

@article{pang2016eosmeter,

   title={An EoS-meter of QCD transition from deep learning},

   author={Pang, Long-Gang and Zhou, Kai and Su, Nan and Petersen, Hannah and Stocker, Horst and Wang, Xin-Nian},

   year={2016},

   month={dec},

   archivePrefix={"arXiv"},

   primaryClass={hep-ph}

}

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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.
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