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

Agni Bethani, Alexander Bitadze, Jonathan Crane, Cinzia Da Via, Nicholas Dann, Sam Dysch, Alessandra Forti, Emily Hanson, James Howarth, David Lack, Ivan Lopez Paz, Jiri Masik, Stephen Menary, Francisca Munoz Sanchez, Alexander Oh, Joleen Pater, Yvonne Peters, Rebecca Pickles, Andrew Pilkington, Darren PriceYang Qin, Jacob Rawling, Nicolas Scharmberg, Savanna Shaw, Terence Wyatt

    Research output: Contribution to journalArticlepeer-review

    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.
    Original languageEnglish
    JournalThe Journal of High Energy Physics
    Early online date30 Apr 2019
    DOIs
    Publication statusPublished - 2019

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