Performance of top-quark andW-boson tagging with ATLAS inRun 2 of the LHC

dc.contributor.authorÇakır, Orhan
dc.contributor.authorDuran Yıldız, Hatice
dc.contributor.departmentFen Fakültesitr_TR
dc.date.accessioned2020-03-18T16:58:56Z
dc.date.available2020-03-18T16:58:56Z
dc.date.issued2019
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.tr_TR
dc.description.indexwos
dc.description.indexScopus
dc.identifier.endpage54tr_TR
dc.identifier.issn/e-issn1434-6044
dc.identifier.issn/e-issn1434-6052
dc.identifier.issue5tr_TR
dc.identifier.other375tr_TR
dc.identifier.startpage01tr_TR
dc.identifier.urihttps://doi.org/10.1140/epjc/s10052-019-6847-8tr_TR
dc.identifier.urihttp://hdl.handle.net/20.500.12575/70736
dc.identifier.volume79tr_TR
dc.language.isoentr_TR
dc.relation.isversionof10.1140/epjc/s10052-019-6847-8tr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıtr_TR
dc.titlePerformance of top-quark andW-boson tagging with ATLAS inRun 2 of the LHCtr_TR
dc.typeArticletr_TR

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