Applications of Deep Neural Networks in a Top Quark Mass Measurement at the LHC

In this analysis the usage of deep neural networks for an improved event selection forthe top-quark-mass measurement in the t¯ muon+jets channel for events at the CMS ext√periment for the LHC run II with a center of mass energy s = 13 TeV was investigated.The composition of the event selection with...

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Autor principal: Lange, Torben
Lenguaje:eng
Publicado: 2018
Materias:
Detectors and Experimental Techniques
Acceso en línea:http://cds.cern.ch/record/2621556
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author Lange, Torben
author_facet Lange, Torben
author_sort Lange, Torben
collection CERN
description In this analysis the usage of deep neural networks for an improved event selection forthe top-quark-mass measurement in the t¯ muon+jets channel for events at the CMS ext√periment for the LHC run II with a center of mass energy s = 13 TeV was investigated.The composition of the event selection with respect to different jet-assignment permutationtypes was found to have a strong influence on the systematic uncertainty of the top-quarkmass measurement. A selection based on the output of neural network trained on classifyingevent permutations of the t¯ muon+jets final state into these permutation types could thentbe used to improve the systematical uncertainty of the current mass measurement from asystematical uncertainty of around 630 MeV to 560 MeV.
id cern-2621556
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2018
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spelling cern-26215562019-09-30T06:29:59Zhttp://cds.cern.ch/record/2621556engLange, TorbenApplications of Deep Neural Networks in a Top Quark Mass Measurement at the LHCDetectors and Experimental TechniquesIn this analysis the usage of deep neural networks for an improved event selection forthe top-quark-mass measurement in the t¯ muon+jets channel for events at the CMS ext√periment for the LHC run II with a center of mass energy s = 13 TeV was investigated.The composition of the event selection with respect to different jet-assignment permutationtypes was found to have a strong influence on the systematic uncertainty of the top-quarkmass measurement. A selection based on the output of neural network trained on classifyingevent permutations of the t¯ muon+jets final state into these permutation types could thentbe used to improve the systematical uncertainty of the current mass measurement from asystematical uncertainty of around 630 MeV to 560 MeV.CMS-TS-2018-004CERN-THESIS-2018-065oai:cds.cern.ch:26215562018
spellingShingle Detectors and Experimental Techniques
Lange, Torben
Applications of Deep Neural Networks in a Top Quark Mass Measurement at the LHC
title Applications of Deep Neural Networks in a Top Quark Mass Measurement at the LHC
title_full Applications of Deep Neural Networks in a Top Quark Mass Measurement at the LHC
title_fullStr Applications of Deep Neural Networks in a Top Quark Mass Measurement at the LHC
title_full_unstemmed Applications of Deep Neural Networks in a Top Quark Mass Measurement at the LHC
title_short Applications of Deep Neural Networks in a Top Quark Mass Measurement at the LHC
title_sort applications of deep neural networks in a top quark mass measurement at the lhc
topic Detectors and Experimental Techniques
url http://cds.cern.ch/record/2621556
work_keys_str_mv AT langetorben applicationsofdeepneuralnetworksinatopquarkmassmeasurementatthelhc

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