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

ATLAS Collaboration

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish
Article number375
JournalEuropean Physical Journal C
Volume79
Issue number5
DOIs
Publication statusPublished - May 1 2019

Fingerprint

Bosons
marking
bosons
quarks
Colliding beam accelerators
Decision trees
showers
substructures
topology
Physics
Topology
physics
optimization
collisions
Experiments
Deep neural networks

All Science Journal Classification (ASJC) codes

  • Engineering (miscellaneous)
  • Physics and Astronomy (miscellaneous)

Cite this

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

In: European Physical Journal C, Vol. 79, No. 5, 375, 01.05.2019.

Research output: Contribution to journalArticle

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