  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.
