Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters.

@article{Paganini2018AcceleratingSW,
  title={Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters.},
  author={Michela Paganini and Luke de Oliveira and Benjamin P. Nachman},
  journal={Physical review letters},
  year={2018},
  volume={120 4},
  pages={
          042003
        }
}
Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of particle collisions to build expectations of what experimental data may look like under different theoretical modeling assumptions. Petabytes of simulated data are needed to develop analysis techniques, though they are expensive to generate using existing algorithms and computing resources. The modeling of detectors and the precise description of particle cascades as they interact with the material in the calorimeter… Expand

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