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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 |
| Sumario: | 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. |
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