An equation-of-state-meter of QCD transition from deep learning
  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 $\rho(p_T,\Phi)$. High-level correlations of $\rho(p_T,\Phi)$ 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. 
Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst St\"ocker and   Xin-Nian Wang
hep-ph cs.LG hep-th nucl-th stat.ML