Corpus ID: 58007017

LHC analysis-specific datasets with Generative Adversarial Networks

@article{Hashemi2019LHCAD,
  title={LHC analysis-specific datasets with Generative Adversarial Networks},
  author={Bobak Hashemi and Nick Amin and Kaustuv Datta and Dominick Olivito and Maurizio Pierini},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.05282}
}
Using generative adversarial networks (GANs), we investigate the possibility of creating large amounts of analysis-specific simulated LHC events at limited computing cost. This kind of generative model is analysis specific in the sense that it directly generates the high-level features used in the last stage of a given physics analyses, learning the N-dimensional distribution of relevant features in the context of a specific analysis selection. We apply this idea to the generation of muon four… Expand
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