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

Research output: Contribution to journalArticle

  • External authors:
  • Agni Bethani
  • Alexander Bitadze
  • Jonathan Crane
  • Cinzia Da Via
  • Nicholas Dann
  • Sam Dysch
  • Alessandra Forti
  • Emily Hanson
  • James Howarth
  • David Lack
  • Ivan Lopez Paz
  • Jiri Masik
  • Stephen Menary
  • Alexander Oh
  • Joleen Pater
  • Yvonne Peters
  • Rebecca Pickles
  • Yang Qin
  • Jacob Rawling
  • Nicolas Scharmberg
  • Savanna Shaw
  • Terence Wyatt

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.

Bibliographical metadata

Original languageEnglish
JournalThe Journal of High Energy Physics
Early online date30 Apr 2019
DOIs
Publication statusPublished - 2019