Performance of top-quark and [Formula omitted]-boson tagging with ATLAS in Run 2 of the LHC

• Published in: The European physical journal. C, Particles and fields Particles and fields, 2019-04, Vol.79 (5), p.1-54
• Main Authors: Aaboud, M, Aad, G, Abbott, B, Abdinov, O, Abeloos, B, Abhayasinghe, D. K, Abidi, S. H
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Bosons; Decision trees; Deconstruction; Large Hadron Collider; Marking; Neural networks; Optimization; Quarks; Substructures; Topology
• ISSN: 1434-6044; 1434-6052
• DOI: 10.1140/epjc/s10052-019-6847-8

The performance of identification algorithms ("taggers") for hadronically decaying top quarks and W bosons in pp collisions at [Formula omitted] = 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 [Formula omitted] for the [Formula omitted] and [Formula omitted] and 36.7 fb [Formula omitted] for the dijet event topologies.