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

The performance of identification algorithms (taggers) for hadronically decaying top quarks and W bosons in pp collisions at = 13TeV 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 optima...

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Main Authors: Aaboud, Morad (Author) , Anders, Christoph (Author) , Andrei, George Victor (Author) , Antel, Claire (Author) , Baas, Alessandra Edda (Author) , Bolz, Arthur (Author) , Brandt, Oleg (Author) , Chouridou, Sofia (Author) , Djuvsland, Julia Isabell (Author) , Dunford, Monica (Author) , Ferreira de Lima, Danilo Enoque (Author) , Franchino, Silvia (Author) , Geisler, Manuel Patrice (Author) , Giulini, Maddalena (Author) , Hanke, Paul (Author) , Jongmanns, Jan (Author) , Kolb, Mathis (Author) , Kugel, Andreas (Author) , Lisovyi, Mykhailo (Author) , Meyer zu Theenhausen, Hanno (Author) , Narrias Villar, Daniel Isaac (Author) , Napolitano, Fabrizio (Author) , S̜ahinsoy, Merve (Author) , Schöning, André (Author) , Schultz-Coulon, Hans-Christian (Author) , Spieker, Thomas (Author) , Stamen, Rainer (Author) , Starovoitov, Pavel (Author) , Suchek, Stanislav (Author)
Corporate Author: ATLAS Collaboration (Author)
Format: Article (Journal)
Language:English
Published: 30 April 2019
In: The European physical journal. C, Particles and fields
Year: 2019, Volume: 79, Issue: 5
ISSN:1434-6052
DOI:10.1140/epjc/s10052-019-6847-8
Online Access:Verlag, Volltext: https://doi.org/10.1140/epjc/s10052-019-6847-8
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Author Notes:ATLAS Collaboration
Description
Summary:The performance of identification algorithms (taggers) for hadronically decaying top quarks and W bosons in pp collisions at = 13TeV 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 36.1fb-1 for the tt and +jet and 36.7-1 for the dijet event topologies.
Item Description:Gesehen am 28.11.2019
The ALICE collaboration: M. Aaboud [und 2875 weitere Personen]
Im Titel ist 'W' kursiv dargestellt
Physical Description:Online Resource
ISSN:1434-6052
DOI:10.1140/epjc/s10052-019-6847-8

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