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Second Generation Machine Learning Based Algorithm for Long-lived Particles Reconstruction in Upgraded LHCb Experiment

The paper presents the developments and preliminary results related to the implementation of a Machine Learning based Algorithm for reconstruction of the long-lived particles in an upgraded LHCb experiment. The analysis is based on a Monte-Carlo simulation prepared for LHC Run 3 data-taking conditio...

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Detalles Bibliográficos
Autor principal: Hashmi, Sabin
Lenguaje:eng
Publicado: 2022
Materias:
Acceso en línea:https://dx.doi.org/10.5506/APhysPolBSupp.15.3-A36
http://cds.cern.ch/record/2839988
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author Hashmi, Sabin
author_facet Hashmi, Sabin
author_sort Hashmi, Sabin
collection CERN
description The paper presents the developments and preliminary results related to the implementation of a Machine Learning based Algorithm for reconstruction of the long-lived particles in an upgraded LHCb experiment. The analysis is based on a Monte-Carlo simulation prepared for LHC Run 3 data-taking conditions. Studied tracks are reconstructed with an official LHCb software application Moore in configuration that is very close to the one that will be operated as a part of the final software trigger system.
id cern-2839988
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2022
record_format invenio
spelling cern-28399882022-11-16T12:29:09Zdoi:10.5506/APhysPolBSupp.15.3-A36http://cds.cern.ch/record/2839988engHashmi, SabinSecond Generation Machine Learning Based Algorithm for Long-lived Particles Reconstruction in Upgraded LHCb ExperimentDetectors and Experimental TechniquesComputing and ComputersThe paper presents the developments and preliminary results related to the implementation of a Machine Learning based Algorithm for reconstruction of the long-lived particles in an upgraded LHCb experiment. The analysis is based on a Monte-Carlo simulation prepared for LHC Run 3 data-taking conditions. Studied tracks are reconstructed with an official LHCb software application Moore in configuration that is very close to the one that will be operated as a part of the final software trigger system.oai:cds.cern.ch:28399882022
spellingShingle Detectors and Experimental Techniques
Computing and Computers
Hashmi, Sabin
Second Generation Machine Learning Based Algorithm for Long-lived Particles Reconstruction in Upgraded LHCb Experiment
title Second Generation Machine Learning Based Algorithm for Long-lived Particles Reconstruction in Upgraded LHCb Experiment
title_full Second Generation Machine Learning Based Algorithm for Long-lived Particles Reconstruction in Upgraded LHCb Experiment
title_fullStr Second Generation Machine Learning Based Algorithm for Long-lived Particles Reconstruction in Upgraded LHCb Experiment
title_full_unstemmed Second Generation Machine Learning Based Algorithm for Long-lived Particles Reconstruction in Upgraded LHCb Experiment
title_short Second Generation Machine Learning Based Algorithm for Long-lived Particles Reconstruction in Upgraded LHCb Experiment
title_sort second generation machine learning based algorithm for long-lived particles reconstruction in upgraded lhcb experiment
topic Detectors and Experimental Techniques
Computing and Computers
url https://dx.doi.org/10.5506/APhysPolBSupp.15.3-A36
http://cds.cern.ch/record/2839988
work_keys_str_mv AT hashmisabin secondgenerationmachinelearningbasedalgorithmforlonglivedparticlesreconstructioninupgradedlhcbexperiment