Particle-identification techniques and performance at LHCb in Run 2

One of the most challenging data analysis tasks of modern High Energy Physics experiments is the identification of particles. In this proceedings we review the new approaches used for particle identification at the LHCb experiment. Machine-Learning based techniques are used to identify the species o...

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Detalles Bibliográficos
Autores principales: Mikhail, Hushchyn, Chekalina, Viktoriia
Lenguaje:eng
Publicado: 2018
Materias:
Particle Physics - Experiment
Acceso en línea:https://dx.doi.org/10.1016/j.nima.2018.10.144
http://cds.cern.ch/record/2629876
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author Mikhail, Hushchyn
Chekalina, Viktoriia
author_facet Mikhail, Hushchyn
Chekalina, Viktoriia
author_sort Mikhail, Hushchyn
collection CERN
description One of the most challenging data analysis tasks of modern High Energy Physics experiments is the identification of particles. In this proceedings we review the new approaches used for particle identification at the LHCb experiment. Machine-Learning based techniques are used to identify the species of charged and neutral particles using several observables obtained by the LHCb sub-detectors. We show the performances of various solutions based on Neural Network and Boosted Decision Tree models.
id cern-2629876
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2018
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spelling cern-26298762022-01-14T15:04:18Zdoi:10.1016/j.nima.2018.10.144http://cds.cern.ch/record/2629876engMikhail, HushchynChekalina, ViktoriiaParticle-identification techniques and performance at LHCb in Run 2Particle Physics - ExperimentOne of the most challenging data analysis tasks of modern High Energy Physics experiments is the identification of particles. In this proceedings we review the new approaches used for particle identification at the LHCb experiment. Machine-Learning based techniques are used to identify the species of charged and neutral particles using several observables obtained by the LHCb sub-detectors. We show the performances of various solutions based on Neural Network and Boosted Decision Tree models.LHCb-PROC-2018-016CERN-LHCb-PROC-2018-016oai:cds.cern.ch:26298762018-07-02
spellingShingle Particle Physics - Experiment
Mikhail, Hushchyn
Chekalina, Viktoriia
Particle-identification techniques and performance at LHCb in Run 2
title Particle-identification techniques and performance at LHCb in Run 2
title_full Particle-identification techniques and performance at LHCb in Run 2
title_fullStr Particle-identification techniques and performance at LHCb in Run 2
title_full_unstemmed Particle-identification techniques and performance at LHCb in Run 2
title_short Particle-identification techniques and performance at LHCb in Run 2
title_sort particle-identification techniques and performance at lhcb in run 2
topic Particle Physics - Experiment
url https://dx.doi.org/10.1016/j.nima.2018.10.144
http://cds.cern.ch/record/2629876
work_keys_str_mv AT mikhailhushchyn particleidentificationtechniquesandperformanceatlhcbinrun2
AT chekalinaviktoriia particleidentificationtechniquesandperformanceatlhcbinrun2

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