  The precise modeling of subatomic particle interactions and propagation
through matter is paramount for the advancement of nuclear and particle physics
searches and precision measurements. The most computationally expensive step in
the simulation pipeline of a typical experiment at the Large Hadron Collider
(LHC) is the detailed modeling of the full complexity of physics processes that
govern the motion and evolution of particle showers inside calorimeters. We
introduce \textsc{CaloGAN}, a new fast simulation technique based on generative
adversarial networks (GANs). We apply these neural networks to the modeling of
electromagnetic showers in a longitudinally segmented calorimeter, and achieve
speedup factors comparable to or better than existing full simulation
techniques on CPU ($100\times$-$1000\times$) and even faster on GPU (up to
$\sim10^5\times$). There are still challenges for achieving precision across
the entire phase space, but our solution can reproduce a variety of geometric
shower shape properties of photons, positrons and charged pions. This
represents a significant stepping stone toward a full neural network-based
detector simulation that could save significant computing time and enable many
analyses now and in the future.
