Particle identification at LHCb: new calibration techniques and machine learning classification algorithms

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<!--HTML-->Particle identification (PID) plays a crucial role in LHCb analyses. Combining information from LHCb subdetectors allows one to distinguish between various species of long-lived charged and neutral particles. PID performance directly affects the sensitivity of most LHCb measurements...

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Detalles Bibliográficos
Autor principal: Lucio Martinez, Miriam
Lenguaje:eng
Publicado: 2018
Materias:
Machine Learning
Acceso en línea:http://cds.cern.ch/record/2312452
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author Lucio Martinez, Miriam
author_facet Lucio Martinez, Miriam
author_sort Lucio Martinez, Miriam
collection CERN
description <!--HTML-->Particle identification (PID) plays a crucial role in LHCb analyses. Combining information from LHCb subdetectors allows one to distinguish between various species of long-lived charged and neutral particles. PID performance directly affects the sensitivity of most LHCb measurements. Advanced multivariate approaches are used at LHCb to obtain the best PID performance and control systematic uncertainties. This talk highlights recent developments in PID that use innovative machine learning techniques, as well as novel data-driven approaches which ensure that PID performance is well reproduced in simulation.
id cern-2312452
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2018
record_format invenio
spelling cern-23124522022-11-02T22:34:03Zhttp://cds.cern.ch/record/2312452engLucio Martinez, MiriamParticle identification at LHCb: new calibration techniques and machine learning classification algorithms2nd IML Machine Learning WorkshopMachine Learning<!--HTML-->Particle identification (PID) plays a crucial role in LHCb analyses. Combining information from LHCb subdetectors allows one to distinguish between various species of long-lived charged and neutral particles. PID performance directly affects the sensitivity of most LHCb measurements. Advanced multivariate approaches are used at LHCb to obtain the best PID performance and control systematic uncertainties. This talk highlights recent developments in PID that use innovative machine learning techniques, as well as novel data-driven approaches which ensure that PID performance is well reproduced in simulation.oai:cds.cern.ch:23124522018
spellingShingle Machine Learning
Lucio Martinez, Miriam
Particle identification at LHCb: new calibration techniques and machine learning classification algorithms
title Particle identification at LHCb: new calibration techniques and machine learning classification algorithms
title_full Particle identification at LHCb: new calibration techniques and machine learning classification algorithms
title_fullStr Particle identification at LHCb: new calibration techniques and machine learning classification algorithms
title_full_unstemmed Particle identification at LHCb: new calibration techniques and machine learning classification algorithms
title_short Particle identification at LHCb: new calibration techniques and machine learning classification algorithms
title_sort particle identification at lhcb: new calibration techniques and machine learning classification algorithms
topic Machine Learning
url http://cds.cern.ch/record/2312452
work_keys_str_mv AT luciomartinezmiriam particleidentificationatlhcbnewcalibrationtechniquesandmachinelearningclassificationalgorithms
AT luciomartinezmiriam 2ndimlmachinelearningworkshop

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