File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

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

TitlePerformance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC
Authors
KeywordsCollisions
Jets
Proton–proton collisions
Issue Date2019
PublisherSpringer Verlag (Germany): SCOAP3. The Journal's web site is located at http://epjc.edpsciences.org
Citation
The European Physical Journal C: Particles and Fields, 2019, v. 79 n. 5, p. article no. 375 How to Cite?
AbstractThe 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.
Persistent Identifierhttp://hdl.handle.net/10722/272943
ISSN
2020 Impact Factor: 4.59
2020 SCImago Journal Rankings: 1.938
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLO, CY-
dc.contributor.authorOrlando, N-
dc.contributor.authorParedes Hernandez, D-
dc.contributor.authorSalvucci, A-
dc.contributor.authorTu, Y-
dc.contributor.authorATLAS Collaboration-
dc.date.accessioned2019-08-06T09:19:32Z-
dc.date.available2019-08-06T09:19:32Z-
dc.date.issued2019-
dc.identifier.citationThe European Physical Journal C: Particles and Fields, 2019, v. 79 n. 5, p. article no. 375-
dc.identifier.issn1434-6044-
dc.identifier.urihttp://hdl.handle.net/10722/272943-
dc.description.abstractThe 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.-
dc.languageeng-
dc.publisherSpringer Verlag (Germany): SCOAP3. The Journal's web site is located at http://epjc.edpsciences.org-
dc.relation.ispartofThe European Physical Journal C: Particles and Fields-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCollisions-
dc.subjectJets-
dc.subjectProton–proton collisions-
dc.titlePerformance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC-
dc.typeArticle-
dc.identifier.emailParedes Hernandez, D: dparedes@hku.hk-
dc.identifier.emailTu, Y: yanjuntu@hku.hk-
dc.identifier.authorityTu, Y=rp01971-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1140/epjc/s10052-019-6847-8-
dc.identifier.scopuseid_2-s2.0-85065123030-
dc.identifier.hkuros299839-
dc.identifier.volume79-
dc.identifier.issue5-
dc.identifier.spagearticle no. 375-
dc.identifier.epagearticle no. 375-
dc.identifier.isiWOS:000466407600007-
dc.publisher.placeGermany-
dc.identifier.issnl1434-6044-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats