Deep-learned top tagging with a Lorentz layer

We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric. With its novel machine learning setup and architecture it allows us to identify boosted top quarks not only from calorimeter towe...

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Bibliographic Details
Main Authors: Butter, Anja (Author) , Kasieczka, Gregor (Author) , Plehn, Tilman (Author) , Russell, Michael (Author)
Format: Article (Journal) Chapter/Article
Language:English
Published: 27 Jul 2017
In: Arxiv

Online Access:Verlag, kostenfrei, Volltext: http://arxiv.org/abs/1707.08966
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Author Notes:Anja Butter, Gregor Kasieczka, Tilman Plehn, and Michael Russell
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Deep-learned top tagging with a Lorentz layer by Butter, Anja (Author) , Kasieczka, Gregor (Author) , Plehn, Tilman (Author) , Russell, Michael (Author) ,


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