Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3061
Title: Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC
Authors: The ATLAS Collaboration
Sultansoy, Saleh
Keywords: Collisions 
 jets 
 proton–proton collisions
Issue Date: 2019
Publisher:  Springer New York LLC
Source: ATLAS Collaboration. (2019). Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC. The European Physical Journal C, 79(5), 375.
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 ?+ jet and 36.7 fb - 1 for the dijet event topologies. © 2019, CERN for the benefit of the ATLAS collaboration.
URI: https://hdl.handle.net/20.500.11851/3061
https://link.springer.com/article/10.1140%2Fepjc%2Fs10052-019-6847-8
ISSN: 14346044
Appears in Collections:Malzeme Bilimi ve Nanoteknoloji Mühendisliği Bölümü / Department of Material Science & Nanotechnology Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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