Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

@article{deOliveira2017LearningPP,
  title={Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis},
  author={Luke de Oliveira and Michela Paganini and Benjamin P. Nachman},
  journal={Computing and Software for Big Science},
  year={2017},
  volume={1},
  pages={1-24}
}
We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in high energy particle physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images—2D representations of energy depositions from particles interacting with a calorimeter. We propose a simple architecture, the Location-Aware Generative Adversarial Network, that learns to produce realistic radiation patterns from simulated high… Expand
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