Aaboud, M.Aad, G.Abbott, B.Abdinov, O.Abeloos, B.Abhayasinghe, D.K.Abidi, S.H.AbouZeid, O.S.Abraham, N.L.Abramowicz, H.Albert, JustinAnelli, Christopher R.Chiu, Y.H.Ghasemi Bostanabad, M.Hamano, KenjiHill, Ewan ChinKeeler, RichardKowalewski, RobertLefebvre, Michelet al.2020-11-102020-11-1020192019Aaboud, M., Aad, G., Abbott, B., Abdinov, O., Abeloos, B., Abhayasinghe, D. K., … Zwalinski, L. (2019). Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC. The European Physical Journal C, 79(5). https://doi.org/10.1140/epjc/s10052-019-6847-8https://doi.org/10.1140/epjc/s10052-019-6847-8http://hdl.handle.net/1828/12329The 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.enArticleDepartment of Physics and Astronomy