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arXiv · 1612.04262

An equation-of-state-meter of QCD transition from deep learning

Long-Gang Pang·Kai Zhou·Nan Su·Hannah Petersen·Horst Stöcker·Xin-Nian Wang
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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 from the simulated final-state particle spectra $ρ(p_T,Φ)$. High-level correlations of $ρ(p_T,Φ)$ 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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Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stöcker, Xin-Nian Wang. 2017-08-02. An equation-of-state-meter of QCD transition from deep learning. https://arxiv.org/abs/1612.04262

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