CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
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Simulation is a key component of physics analysis in particle physics and nuclear physics. The most computationally expensive simulation step is the detailed modeling of particle showers inside calorimeters. Full detector simulations are too slow to meet the growing demands resulting from large quantities of data; current fast simulations are not precise enough to serve the entire physics program. Therefore, we introduce CaloGAN, a new fast simulation based on generative adversarial neural networks (GANs). We apply the CaloGAN to model electromagnetic showers in a longitudinally segmented calorimeter. 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. In particular, the CaloGAN achieves speedup factors comparable to or better than existing fast simulation techniques on CPU (100×-1000×) and even faster on GPU (up to ∼10^5×)) and has the capability of faithfully reproducing many aspects of key shower shape variables for a variety of particle types.
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