Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/123565
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Type: Journal article
Title: Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC
Author: Aaboud, M.
Aad, G.
Abbott, B.
Abdinov, O.
Abeloos, B.
Abhayasinghe, D.K.
Abidi, S.H.
AbouZeid, O.S.
Abraham, N.L.
Abramowicz, H.
Abreu, H.
Abulaiti, Y.
Acharya, B.S.
Adachi, S.
Adam, L.
Adamczyk, L.
Adelman, J.
Adersberger, M.
Adiguzel, A.
Adye, T.
et al.
Citation: European Physical Journal C: Particles and Fields, 2019; 79(5):375-1-375-54
Publisher: Springer Nature
Issue Date: 2019
ISSN: 1434-6044
1434-6052
Statement of
Responsibility: 
M. Aaboud ... D. Duvnjak ... P. Jackson ... J.L. Oliver ... A. Petridis ... A. Qureshi ... A.S. Sharma ... M.J. White ... et al. [The ATLAS Collaboration]
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.
Rights: © CERN for the benefit of the ATLAS collaboration 2019. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Funded by SCOAP³.
DOI: 10.1140/epjc/s10052-019-6847-8
Grant ID: ARC
Published version: http://dx.doi.org/10.1140/epjc/s10052-019-6847-8
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