Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC
Aaboud, M ; Åkesson, Torsten LU
; Bocchetta, Simona
LU
; Corrigan, Eric
LU
; Doglioni, Caterina
LU
; Gregersen, Kristian
LU
; Brottmann Hansen, Eva
LU
; Hedberg, Vincent
LU
; Jarlskog, Göran
LU
and Kalderon, Charles
LU
, et al.
(2019)
In European Physical Journal C
79(5).
- 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... (More)
- 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. © 2019, CERN for the benefit of the ATLAS collaboration. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/c4e83bd9-baec-43cd-8aca-2652cf55fc07
- author
- Aaboud, M
; Åkesson, Torsten
LU
; Bocchetta, Simona
LU
; Corrigan, Eric
LU
; Doglioni, Caterina
LU
; Gregersen, Kristian
LU
; Brottmann Hansen, Eva
LU
; Hedberg, Vincent
LU
; Jarlskog, Göran
LU
and Kalderon, Charles
LU
, et al. (More)
- Aaboud, M
; Åkesson, Torsten
LU
; Bocchetta, Simona
LU
; Corrigan, Eric
LU
; Doglioni, Caterina
LU
; Gregersen, Kristian
LU
; Brottmann Hansen, Eva
LU
; Hedberg, Vincent
LU
; Jarlskog, Göran
LU
; Kalderon, Charles
LU
; Kellermann, Edgar
LU
; Konya, Balazs
LU
; Lytken, Else
LU
; Mankinen, Katja
LU
; Mjörnmark, Ulf
LU
; Mullier, Geoffrey
LU
; Pöttgen, Ruth
LU
; Poulsen, Trine
LU
; Smirnova, Oxana
LU
and Zwalinski, L
(Less)
- author collaboration
- organization
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- in
- European Physical Journal C
- volume
- 79
- issue
- 5
- article number
- 375
- publisher
- Springer
- external identifiers
-
- scopus:85065123030
- ISSN
- 1434-6044
- DOI
- 10.1140/epjc/s10052-019-6847-8
- language
- English
- LU publication?
- yes
- additional info
- Export Date: 14 May 2019
- id
- c4e83bd9-baec-43cd-8aca-2652cf55fc07
- date added to LUP
- 2019-05-14 15:08:14
- date last changed
- 2023-04-09 10:12:26
@article{c4e83bd9-baec-43cd-8aca-2652cf55fc07,
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. © 2019, CERN for the benefit of the ATLAS collaboration.}},
author = {{Aaboud, M and Åkesson, Torsten and Bocchetta, Simona and Corrigan, Eric and Doglioni, Caterina and Gregersen, Kristian and Brottmann Hansen, Eva and Hedberg, Vincent and Jarlskog, Göran and Kalderon, Charles and Kellermann, Edgar and Konya, Balazs and Lytken, Else and Mankinen, Katja and Mjörnmark, Ulf and Mullier, Geoffrey and Pöttgen, Ruth and Poulsen, Trine and Smirnova, Oxana and Zwalinski, L}},
issn = {{1434-6044}},
language = {{eng}},
number = {{5}},
publisher = {{Springer}},
series = {{European Physical Journal C}},
title = {{Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC}},
url = {{http://dx.doi.org/10.1140/epjc/s10052-019-6847-8}},
doi = {{10.1140/epjc/s10052-019-6847-8}},
volume = {{79}},
year = {{2019}},
}