Performance of top-quark and 𝑊𝑊-boson tagging with ATLAS in Run 2 of the LHC
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Issue Date
2019-04-30Author
Aaboud, M.
Rogan, Christopher
ATLAS Collaboration
Publisher
SpringerOpen
Type
Article
Article Version
Scholarly/refereed, publisher version
Rights
© CERN for the benefit of the ATLAS collaboration 2019.
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The performance of identification algorithms (“taggers”) for hadronically decaying top quarks and W bosons in pp collisions at 𝑠√ = 13 TeV recorded by the ATLAS experiment at the Large Hadron Collider is presented. A set of techniques based on jet shape observables are studied to determine a set of optimal cut-based taggers for use in physics analyses. The studies are extended to assess the utility of combinations of substructure observables as a multivariate tagger using boosted decision trees or deep neural networks in comparison with taggers based on two-variable combinations. In addition, for highly boosted top-quark tagging, a deep neural network based on jet constituent inputs as well as a re-optimisation of the shower deconstruction technique is presented. The performance of these taggers is studied in data collected during 2015 and 2016 corresponding to 36.1 fb−1 for the 𝑡𝑡¯ and 𝛾+jet and 36.7 fb−1 for the dijet event topologies.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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Citation
Aaboud, M., Aad, G., Abbott, B. et al. Performance of top-quark and 𝑊𝑊-boson tagging with ATLAS in Run 2 of the LHC. Eur. Phys. J. C 79, 375 (2019). https://doi.org/10.1140/epjc/s10052-019-6847-8
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