Skip to main content
eScholarship
Open Access Publications from the University of California

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

  • Author(s): Aaboud, M
  • Aad, G
  • Abbott, B
  • Abdinov, O
  • Abeloos, B
  • Abhayasinghe, DK
  • Abidi, SH
  • AbouZeid, OS
  • Abraham, NL
  • Abramowicz, H
  • Abreu, H
  • Abulaiti, Y
  • Acharya, BS
  • Adachi, S
  • Adam, L
  • Adamczyk, L
  • Adelman, J
  • Adersberger, M
  • Adiguzel, A
  • Adye, T
  • Affolder, AA
  • Afik, Y
  • Agheorghiesei, C
  • Aguilar-Saavedra, JA
  • Ahmadov, F
  • Aielli, G
  • Akatsuka, S
  • Åkesson, TPA
  • Akilli, E
  • Akimov, AV
  • Alberghi, GL
  • Albert, J
  • Albicocco, P
  • Alconada Verzini, MJ
  • Alderweireldt, S
  • Aleksa, M
  • Aleksandrov, IN
  • Alexa, C
  • Alexopoulos, T
  • Alhroob, M
  • Ali, B
  • Alimonti, G
  • Alison, J
  • Alkire, SP
  • Allaire, C
  • Allbrooke, BMM
  • Allen, BW
  • Allport, PP
  • Aloisio, A
  • Alonso, A
  • Alonso, F
  • Alpigiani, C
  • Alshehri, AA
  • Alstaty, MI
  • Alvarez Gonzalez, B
  • Álvarez Piqueras, D
  • Alviggi, MG
  • Amadio, BT
  • Amaral Coutinho, Y
  • Ambler, A
  • Ambroz, L
  • Amelung, C
  • Amidei, D
  • Amor Dos Santos, SP
  • Amoroso, S
  • Amrouche, CS
  • Anastopoulos, C
  • Ancu, LS
  • Andari, N
  • Andeen, T
  • Anders, CF
  • Anders, JK
  • Anderson, KJ
  • Andreazza, A
  • Andrei, V
  • Anelli, CR
  • Angelidakis, S
  • Angelozzi, I
  • Angerami, A
  • Anisenkov, AV
  • Annovi, A
  • Antel, C
  • Anthony, MT
  • Antonelli, M
  • Antrim, DJA
  • Anulli, F
  • Aoki, M
  • Pozo, JAA
  • Aperio Bella, L
  • Arabidze, G
  • Araque, JP
  • Araujo Ferraz, V
  • Araujo Pereira, R
  • Arce, ATH
  • Ardell, RE
  • Arduh, FA
  • Arguin, JF
  • Argyropoulos, S
  • Armbruster, AJ
  • Armitage, LJ
  • et al.
Abstract

© 2019, CERN for the benefit of the ATLAS collaboration. 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.

Main Content
Current View