End-to-end Deep Learning Inference in CMS software framework

Deep learning techniques have been proven to provide excellent performance for a variety of high energy physics applications, such as particle identification, event reconstruction and trigger operations. Using low-level detector information in end-to-end deep learning approach allows to probe the po...

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Autor principal: CMS Collaboration
Lenguaje:eng
Publicado: 2023
Materias:
Detectors and Experimental Techniques
Acceso en línea:http://cds.cern.ch/record/2863315
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author CMS Collaboration
author_facet CMS Collaboration
author_sort CMS Collaboration
collection CERN
description Deep learning techniques have been proven to provide excellent performance for a variety of high energy physics applications, such as particle identification, event reconstruction and trigger operations. Using low-level detector information in end-to-end deep learning approach allows to probe the poorly explored regions for dark matter search. This note presents an implementation of the end-to-end deep learning inference framework in CMS Software framework (CMSSW) for various physics objects classifiers such as electron/photon, quark/gluon, top and tau. The inference is benchmarked on CPU and GPUs.
id cern-2863315
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
record_format invenio
spelling cern-28633152023-06-29T20:24:42Zhttp://cds.cern.ch/record/2863315engCMS CollaborationEnd-to-end Deep Learning Inference in CMS software frameworkDetectors and Experimental TechniquesDeep learning techniques have been proven to provide excellent performance for a variety of high energy physics applications, such as particle identification, event reconstruction and trigger operations. Using low-level detector information in end-to-end deep learning approach allows to probe the poorly explored regions for dark matter search. This note presents an implementation of the end-to-end deep learning inference framework in CMS Software framework (CMSSW) for various physics objects classifiers such as electron/photon, quark/gluon, top and tau. The inference is benchmarked on CPU and GPUs.CMS-DP-2023-036CERN-CMS-DP-2023-036oai:cds.cern.ch:28633152023-06-16
spellingShingle Detectors and Experimental Techniques
CMS Collaboration
End-to-end Deep Learning Inference in CMS software framework
title End-to-end Deep Learning Inference in CMS software framework
title_full End-to-end Deep Learning Inference in CMS software framework
title_fullStr End-to-end Deep Learning Inference in CMS software framework
title_full_unstemmed End-to-end Deep Learning Inference in CMS software framework
title_short End-to-end Deep Learning Inference in CMS software framework
title_sort end-to-end deep learning inference in cms software framework
topic Detectors and Experimental Techniques
url http://cds.cern.ch/record/2863315
work_keys_str_mv AT cmscollaboration endtoenddeeplearninginferenceincmssoftwareframework

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