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

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## Abstract

The performance of identification algorithms (“taggers”) for hadronically decaying top quarks and W bosons in pp collisions at $\sqrt{s}$ = 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 $t\bar{t}$ and $\gamma +\text {jet}$ and 36.7 fb$^{-1}$ for the dijet event topologies.
Original language English Aaboud:2018psm 375 European Physical Journal C: Particles and Fields C79 5 https://doi.org/10.1140/epjc/s10052-019-6847-8 Published - 30 Apr 2019

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