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.
Aaboud, M., Aad, G., Abbott, B., Abdinov, O., Abeloos, B., Abhayasinghe, D.K., et al. (2019). Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC. THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS, 79(5) [10.1140/epjc/s10052-019-6847-8].