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

Abraham, N L, Allbrooke, B M M, Asquith, L, Cerri, A, Jones, S D, De Santo, A, Salvatore, F, Shaw, K, Stevenson, T J, Suruliz, K, Sutton, M R, Tresoldi, F, Trovato, F, Vivarelli, I, Winkels, E, The ATLAS Collaboration, and others, (2019) Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC. European Physical Journal C - Particles and Fields, 79 (5). a375 1-54. ISSN 1434-6044

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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 −1 for the dijet event topologies.

Item Type: Article
Schools and Departments: School of Mathematical and Physical Sciences > Physics and Astronomy
Subjects: Q Science > QC Physics
Depositing User: Amelia Redman
Date Deposited: 20 Sep 2019 11:12
Last Modified: 20 Sep 2019 11:15
URI: http://sro.sussex.ac.uk/id/eprint/85930

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