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|Title:||Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC|
|Citation:||European Physical Journal C, 2019; 79(5):375-1-375-54|
|M. Aaboud ... D. Duvnjak ... P. Jackson ... J.L. Oliver ... A. Petridis ... A. Qureshi ... A.S. Sharma ... M.J. White ... et al. [The ATLAS Collaboration]|
|Abstract:||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.|
|Rights:||© CERN for the benefit of the ATLAS collaboration 2019. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Funded by SCOAP³.|
|Appears in Collections:||Physics publications|
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