Jet Constituents for Deep Neural Network Based Top Quark Tagging

Abstract 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 previous approaches, this strategy does not result in a loss of information during pixelisation or the calculation of high level features. New preprocessing methods that do not alter key physical quantities such as the jet mass are developed. The jet classification method achieves background rejection of 45 for 50% efficiency operating point for reconstruction level jets with transverse momentum range of 600 to 2500 GeV and is insensitive to multiple proton-proton interactions at the levels expected throughout LHC Run 2.