Performance of Run 3 track reconstruction with the mkFit algorithm

This note reports on the CMS track reconstruction at the LHC Run 3. In Run 2, the CMS track reconstruction algorithm used an iterative approach based on combinatorial Kalman Filter (CKF). For Run 3, a new algorithm has been developed for track pattern recognition, named mkFit, that maximally exploi...

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Detalles Bibliográficos
Autor principal: CMS Collaboration
Lenguaje:eng
Publicado: 2022
Materias:
Detectors and Experimental Techniques
Acceso en línea:http://cds.cern.ch/record/2814000
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author CMS Collaboration
author_facet CMS Collaboration
author_sort CMS Collaboration
collection CERN
description This note reports on the CMS track reconstruction at the LHC Run 3. In Run 2, the CMS track reconstruction algorithm used an iterative approach based on combinatorial Kalman Filter (CKF). For Run 3, a new algorithm has been developed for track pattern recognition, named mkFit, that maximally exploits parallelization and vectorization in multi-core CPU architectures. This algorithm has been deployed in the CMS software for a subset of tracking iterations. The mkFit algorithm allows to retain a similar physics performance with respect to the traditional CKF-based pattern recognition, while improving the computational performance of the CMS track reconstruction. The content of this note is also available at: twiki.cern.ch/twiki/bin/view/CMSPublic/TRKmkFitRun3
id cern-2814000
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2022
record_format invenio
spelling cern-28140002023-04-06T20:32:30Zhttp://cds.cern.ch/record/2814000engCMS CollaborationPerformance of Run 3 track reconstruction with the mkFit algorithmDetectors and Experimental TechniquesThis note reports on the CMS track reconstruction at the LHC Run 3. In Run 2, the CMS track reconstruction algorithm used an iterative approach based on combinatorial Kalman Filter (CKF). For Run 3, a new algorithm has been developed for track pattern recognition, named mkFit, that maximally exploits parallelization and vectorization in multi-core CPU architectures. This algorithm has been deployed in the CMS software for a subset of tracking iterations. The mkFit algorithm allows to retain a similar physics performance with respect to the traditional CKF-based pattern recognition, while improving the computational performance of the CMS track reconstruction. The content of this note is also available at: twiki.cern.ch/twiki/bin/view/CMSPublic/TRKmkFitRun3CMS-DP-2022-018CERN-CMS-DP-2022-018oai:cds.cern.ch:28140002022-06-27
spellingShingle Detectors and Experimental Techniques
CMS Collaboration
Performance of Run 3 track reconstruction with the mkFit algorithm
title Performance of Run 3 track reconstruction with the mkFit algorithm
title_full Performance of Run 3 track reconstruction with the mkFit algorithm
title_fullStr Performance of Run 3 track reconstruction with the mkFit algorithm
title_full_unstemmed Performance of Run 3 track reconstruction with the mkFit algorithm
title_short Performance of Run 3 track reconstruction with the mkFit algorithm
title_sort performance of run 3 track reconstruction with the mkfit algorithm
topic Detectors and Experimental Techniques
url http://cds.cern.ch/record/2814000
work_keys_str_mv AT cmscollaboration performanceofrun3trackreconstructionwiththemkfitalgorithm

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