• Corpus ID: 1152906

Jet Constituents for Deep Neural Network Based Top Quark Tagging

@article{Pearkes2017JetCF,
  title={Jet Constituents for Deep Neural Network Based Top Quark Tagging},
  author={Jannicke Pearkes and Wojciech Fedorko and Alison Lister and Colin Gay},
  journal={ArXiv},
  year={2017},
  volume={abs/1704.02124}
}
Recent literature on deep neural networks for tagging of highly energetic jets resulting from top quark decays has focused on image based techniques or multivariate approaches using high-level jet substructure variables. Here, a sequential approach to this task is taken by using an ordered sequence of jet constituents as training inputs. Unlike the majority of previous approaches, this strategy does not result in a loss of information during pixelisation or the calculation of high level… 

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