The Machine Learning landscape of top taggers

  title={The Machine Learning landscape of top taggers},
  author={Gregor Kasieczka and Tilman Plehn and Anja Butter and Dipsikha Debnath and M. Fairbairn and Wojciech Fedorko and Colin Gay and Loukas Gouskos and Patrick T. Komiske and S. Leiss and Alison Lister and Sebastian Macaluso and Eric M. Metodiev and Liam Moore and Benjamin Philip Nachman and Karl Nordstrom and Jannicke Pearkes and Huilin Qu and Yannik Rath and M A Riegler and David Shih and J. Thompson and Sreedevi Varma},
  journal={SciPost Physics},
Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun. 

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