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Physicists at the Large Hadron Collider (LHC) rely on detailed simulations ofparticle collisions to build expectations of what experimental data may looklike under different theory modeling assumptions. Petabytes of simulated dataare needed to develop analysis techniques, though they are expensive togenerate using existing algorithms and computing resources. The modeling ofdetectors and the precise description of particle cascades as they interactwith the material in the calorimeter are the most computationally demandingsteps in the simulation pipeline. We therefore introduce a deep neuralnetwork-based generative model to enable high-fidelity, fast, electromagneticcalorimeter simulation. There are still challenges for achieving precisionacross the entire phase space, but our current solution can reproduce a varietyof particle shower properties while achieving speed-up factors of up to100,000$\times$. This opens the door to a new era of fast simulation that couldsave significant computing time and disk space, while extending the reach ofphysics searches and precision measurements at the LHC and beyond.

Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters