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The precise modeling of subatomic particle interactions and propagationthrough matter is paramount for the advancement of nuclear and particle physicssearches and precision measurements. The most computationally expensive step inthe simulation pipeline of a typical experiment at the Large Hadron Collider(LHC) is the detailed modeling of the full complexity of physics processes thatgovern the motion and evolution of particle showers inside calorimeters. Weintroduce \textsc{CaloGAN}, a new fast simulation technique based on generativeadversarial networks (GANs). We apply these neural networks to the modeling ofelectromagnetic showers in a longitudinally segmented calorimeter, and achievespeedup factors comparable to or better than existing full simulationtechniques on CPU ($100\times$-$1000\times$) and even faster on GPU (up to$\sim10^5\times$). There are still challenges for achieving precision acrossthe entire phase space, but our solution can reproduce a variety of geometricshower shape properties of photons, positrons and charged pions. Thisrepresents a significant stepping stone toward a full neural network-baseddetector simulation that could save significant computing time and enable manyanalyses now and in the future.

CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks