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Balazs Konya

Researcher

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

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

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.

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

Document type

Journal article

Publisher

Springer

Topic

  • Subatomic Physics

Status

Published

ISBN/ISSN/Other

  • ISSN: 1434-6044