Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC

ATLAS Collaboration, Paul Newman

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25 Citations (Scopus)
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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 tt̅ and γ+jet and 36.7 fb−1 for the dijet event topologies.
Original languageEnglish
Article number375
Number of pages54
JournalEur. Phys. J. C
Issue number5
Publication statusPublished - 30 Apr 2019

Bibliographical note

79 pages in total, author list starting page 63, 39 figures, 6 tables, submitted to The European Physical Journal C. All figures including auxiliary figures are available at


  • hep-ex


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