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

Summary, in English

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.

• Author: M Aaboud; Torsten Åkesson; Simona Bocchetta; Eric Corrigan; Caterina Doglioni; Kristian Gregersen; Eva Brottmann Hansen; Vincent Hedberg; Göran Jarlskog; Charles Kalderon; Edgar Kellermann; Balazs Konya; Else Lytken; Katja Mankinen; Ulf Mjörnmark; Geoffrey Mullier; Ruth Pöttgen; Trine Poulsen; Oxana Smirnova; L Zwalinski
• Department/s: Particle and nuclear physics; eSSENCE: The e-Science Collaboration
• Publishing year: 2019
• Language: English
• Publication/Series: European Physical Journal C
• Volume: 79
• Issue: 5
• Links: http://dx.doi.org/10.1140/epjc/s10052-019-6847-8
• Document type: Journal article
• Publisher: Springer
• Topic: Subatomic Physics
• Status: Published
• ISBN/ISSN/Other: ISSN: 1434-6044