Machine Learning in Top Physics in the ATLAS and CMS Collaborations

Machine learning is essential in many aspects of top-quark related physics in the ATLAS and CMS Collaborations. This work aims to give a brief overview over current applications in the two collaborations as well as on-going studies for future applications.\footnote{Copyright 2023 CERN for the benefi...

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Autor principal: Keicher, Philip Daniel
Lenguaje:eng
Publicado: 2023
Materias:
Detectors and Experimental Techniques
Acceso en línea:http://cds.cern.ch/record/2847691
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author Keicher, Philip Daniel
author_facet Keicher, Philip Daniel
author_sort Keicher, Philip Daniel
collection CERN
description Machine learning is essential in many aspects of top-quark related physics in the ATLAS and CMS Collaborations. This work aims to give a brief overview over current applications in the two collaborations as well as on-going studies for future applications.\footnote{Copyright 2023 CERN for the benefit of the ATLAS and CMS Collaborations. Reproduction of this article or parts of it is allowed as specified in the CC-BY-4.0 license}
id cern-2847691
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
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spelling cern-28476912023-01-28T01:42:12Zhttp://cds.cern.ch/record/2847691engKeicher, Philip DanielMachine Learning in Top Physics in the ATLAS and CMS CollaborationsDetectors and Experimental TechniquesMachine learning is essential in many aspects of top-quark related physics in the ATLAS and CMS Collaborations. This work aims to give a brief overview over current applications in the two collaborations as well as on-going studies for future applications.\footnote{Copyright 2023 CERN for the benefit of the ATLAS and CMS Collaborations. Reproduction of this article or parts of it is allowed as specified in the CC-BY-4.0 license}CMS-CR-2023-006oai:cds.cern.ch:28476912023-01-18
spellingShingle Detectors and Experimental Techniques
Keicher, Philip Daniel
Machine Learning in Top Physics in the ATLAS and CMS Collaborations
title Machine Learning in Top Physics in the ATLAS and CMS Collaborations
title_full Machine Learning in Top Physics in the ATLAS and CMS Collaborations
title_fullStr Machine Learning in Top Physics in the ATLAS and CMS Collaborations
title_full_unstemmed Machine Learning in Top Physics in the ATLAS and CMS Collaborations
title_short Machine Learning in Top Physics in the ATLAS and CMS Collaborations
title_sort machine learning in top physics in the atlas and cms collaborations
topic Detectors and Experimental Techniques
url http://cds.cern.ch/record/2847691
work_keys_str_mv AT keicherphilipdaniel machinelearningintopphysicsintheatlasandcmscollaborations

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