Collaboration, ATLAS and Barton, A.E. and Bertram, I.A. and Borissov, G. and Bouhova-Thacker, E.V. and Fox H.AU - Henderson, R.C.W. and Jones, R.W.L. and Kartvelishvili, V. and Long, R.E. and Love, P.A. and Muenstermann, D. and Parker, A.J. and Smizanska, M. and Tee, A.S. and Walder, J. and Wharton, A.M. and Whitmore, B.W. (2019) Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC. European Physical Journal D, 79 (5): 375. ISSN 1434-6060
Full text not available from this repository.Abstract
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. © 2019, CERN for the benefit of the ATLAS collaboration.