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Jet Constituents for Deep Neural Network Based Top Quark Tagging
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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 features. The jet classification method achieves a background rejection of 45 at a 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 Run 2 of the LHC.
Forward citations
Cited by 3 Pith papers
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Boosted $W/Z$ Tagging with Jet Charge and Deep Learning
Jet charge as an input channel improves deep-learning W+/W-/Z classification, and a dual-CNN architecture gives the largest gains for Z versus W discrimination.
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JEDI-net: a jet identification algorithm based on interaction networks
JEDI-net, an interaction-network jet tagger, outperforms DNN, CNN, and GRU taggers on a five-class simulated LHC jet dataset.
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