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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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Lenguaje: | eng |
Publicado: |
2022
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Acceso en línea: | https://dx.doi.org/10.5506/APhysPolBSupp.15.3-A36 http://cds.cern.ch/record/2839988 |
_version_ | 1780976009487581184 |
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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 |