Title
Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC
Date Issued
01 May 2019
Access level
open access
Resource Type
journal article
Author(s)
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.
Affolder A.A.
Afik Y.
Agheorghiesei C.
Aguilar-Saavedra J.A.
Ahmadov F.
Aielli G.
Akatsuka S.
Åkesson T.P.A.
Akilli E.
Akimov A.V.
Alberghi G.L.
Albert J.
Albicocco P.
Alconada Verzini M.J.
Alderweireldt S.
Aleksa M.
Aleksandrov I.N.
Alexa C.
Alexopoulos T.
Alhroob M.
Ali B.
Alimonti G.
Alison J.
Alkire S.P.
Allaire C.
Allbrooke B.M.M.
Allen B.W.
Allport P.P.
Aloisio A.
Alonso A.
Alonso F.
Alpigiani C.
Alshehri A.A.
Alstaty M.I.
Alvarez Gonzalez B.
Álvarez Piqueras D.
Alviggi M.G.
Amadio B.T.
Amaral Coutinho Y.
Ambler A.
Ambroz L.
Amelung C.
Amidei D.
Amor Dos Santos S.P.
Amoroso S.
Amrouche C.S.
Anastopoulos C.
Ancu L.S.
Andari N.
Andeen T.
Anders C.F.
Anders J.K.
Anderson K.J.
Andreazza A.
Andrei V.
Anelli C.R.
Angelidakis S.
Angelozzi I.
Angerami A.
Anisenkov A.V.
Annovi A.
Antel C.
Anthony M.T.
Antonelli M.
Antrim D.J.A.
Anulli F.
Aoki M.
Pozo J.A.A.
Aperio Bella L.
Arabidze G.
Araque J.P.
Araujo Ferraz V.
Araujo Pereira R.
Arce A.T.H.
Ardell R.E.
Arduh F.A.
Arguin J.F.
Argyropoulos S.
Armbruster A.J.
Armitage L.J.
Publisher(s)
Springer New York LLC
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.
Volume
79
Issue
5
Language
English
OCDE Knowledge area
Física de partículas, Campos de la Física
Subjects
Scopus EID
2-s2.0-85065123030
Source
European Physical Journal C
ISSN of the container
14346044
Sponsor(s)
Science and Technology Facilities Council (GRIDPP) and Japan Society for the Promotion of Science (17H02902).
Sources of information:
Directorio de Producción Científica
Scopus