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

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 t t and γ + jet and 36.7 fb -1 for the dijet event topologies.