Identification of boosted top quarks and W bosons with Machine learning in ATLAS

We present techniques for the identification of hadronically-decaying W bosons and top quarks using high-level features as inputs to boosted decision trees and deep neural networks in the ATLAS experiment at sqrt(s)=13 TeV. The performance of these machine learning based taggers is compared in Monte...

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Detalles Bibliográficos
Autor principal: Majersky, Oliver
Lenguaje:eng
Publicado: 2017
Materias:
Particle Physics - Experiment
Acceso en línea:http://cds.cern.ch/record/2275501
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author Majersky, Oliver
author_facet Majersky, Oliver
author_sort Majersky, Oliver
collection CERN
description We present techniques for the identification of hadronically-decaying W bosons and top quarks using high-level features as inputs to boosted decision trees and deep neural networks in the ATLAS experiment at sqrt(s)=13 TeV. The performance of these machine learning based taggers is compared in Monte Carlo simulation with various different tagging algorithms. An improvement in background rejection with respect to different taggers is observed. In addition, the performance of the machine learning taggers is examined in full Run-II data set in top quark pair, dijet and photon+jet topologies.
id cern-2275501
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2017
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spelling cern-22755012019-09-30T06:29:59Zhttp://cds.cern.ch/record/2275501engMajersky, OliverIdentification of boosted top quarks and W bosons with Machine learning in ATLASParticle Physics - ExperimentWe present techniques for the identification of hadronically-decaying W bosons and top quarks using high-level features as inputs to boosted decision trees and deep neural networks in the ATLAS experiment at sqrt(s)=13 TeV. The performance of these machine learning based taggers is compared in Monte Carlo simulation with various different tagging algorithms. An improvement in background rejection with respect to different taggers is observed. In addition, the performance of the machine learning taggers is examined in full Run-II data set in top quark pair, dijet and photon+jet topologies.ATL-PHYS-SLIDE-2017-573oai:cds.cern.ch:22755012017-07-23
spellingShingle Particle Physics - Experiment
Majersky, Oliver
Identification of boosted top quarks and W bosons with Machine learning in ATLAS
title Identification of boosted top quarks and W bosons with Machine learning in ATLAS
title_full Identification of boosted top quarks and W bosons with Machine learning in ATLAS
title_fullStr Identification of boosted top quarks and W bosons with Machine learning in ATLAS
title_full_unstemmed Identification of boosted top quarks and W bosons with Machine learning in ATLAS
title_short Identification of boosted top quarks and W bosons with Machine learning in ATLAS
title_sort identification of boosted top quarks and w bosons with machine learning in atlas
topic Particle Physics - Experiment
url http://cds.cern.ch/record/2275501
work_keys_str_mv AT majerskyoliver identificationofboostedtopquarksandwbosonswithmachinelearninginatlas

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