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Machine learning and parallelism in the reconstruction of LHCb and its upgrade

The LHCb detector at the LHC is a general purpose detector in the forward region with a focus on reconstructing decays of c- and b-hadrons. For Run II of the LHC, a new trigger strategy with a real-time reconstruction, alignment and calibration was employed. This was made possible by implementing an...

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Autor principal: De Cian, Michel
Lenguaje:eng
Publicado: 2016
Materias:
Acceso en línea:https://dx.doi.org/10.1051/epjconf/201612700006
http://cds.cern.ch/record/2205691
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author De Cian, Michel
author_facet De Cian, Michel
author_sort De Cian, Michel
collection CERN
description The LHCb detector at the LHC is a general purpose detector in the forward region with a focus on reconstructing decays of c- and b-hadrons. For Run II of the LHC, a new trigger strategy with a real-time reconstruction, alignment and calibration was employed. This was made possible by implementing an oine-like track reconstruction in the high level trigger. However, the ever increasing need for a higher throughput and the move to parallelism in the CPU architectures in the last years necessitated the use of vectorization techniques to achieve the desired speed and a more extensive use of machine learning to veto bad events early on. This document discusses selected improvements in computationally expensive parts of the track reconstruction, like the Kalman filter, as well as an improved approach to get rid of fake tracks using fast machine learning techniques. In the last part, a short overview of the track reconstruction challenges for the upgrade of LHCb, is given. Running a fully software-based trigger, a large gain in speed in the reconstruction has to be achieved to cope with the 40MHz bunch-crossing rate. Two possible approaches for techniques exploiting massive parallelization are discussed.
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institution Organización Europea para la Investigación Nuclear
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spelling cern-22056912019-09-30T06:29:59Zdoi:10.1051/epjconf/201612700006http://cds.cern.ch/record/2205691engDe Cian, MichelMachine learning and parallelism in the reconstruction of LHCb and its upgradeParticle Physics - ExperimentThe LHCb detector at the LHC is a general purpose detector in the forward region with a focus on reconstructing decays of c- and b-hadrons. For Run II of the LHC, a new trigger strategy with a real-time reconstruction, alignment and calibration was employed. This was made possible by implementing an oine-like track reconstruction in the high level trigger. However, the ever increasing need for a higher throughput and the move to parallelism in the CPU architectures in the last years necessitated the use of vectorization techniques to achieve the desired speed and a more extensive use of machine learning to veto bad events early on. This document discusses selected improvements in computationally expensive parts of the track reconstruction, like the Kalman filter, as well as an improved approach to get rid of fake tracks using fast machine learning techniques. In the last part, a short overview of the track reconstruction challenges for the upgrade of LHCb, is given. Running a fully software-based trigger, a large gain in speed in the reconstruction has to be achieved to cope with the 40MHz bunch-crossing rate. Two possible approaches for techniques exploiting massive parallelization are discussed.LHCb-PROC-2016-019CERN-LHCb-PROC-2016-019oai:cds.cern.ch:22056912016-08-05
spellingShingle Particle Physics - Experiment
De Cian, Michel
Machine learning and parallelism in the reconstruction of LHCb and its upgrade
title Machine learning and parallelism in the reconstruction of LHCb and its upgrade
title_full Machine learning and parallelism in the reconstruction of LHCb and its upgrade
title_fullStr Machine learning and parallelism in the reconstruction of LHCb and its upgrade
title_full_unstemmed Machine learning and parallelism in the reconstruction of LHCb and its upgrade
title_short Machine learning and parallelism in the reconstruction of LHCb and its upgrade
title_sort machine learning and parallelism in the reconstruction of lhcb and its upgrade
topic Particle Physics - Experiment
url https://dx.doi.org/10.1051/epjconf/201612700006
http://cds.cern.ch/record/2205691
work_keys_str_mv AT decianmichel machinelearningandparallelisminthereconstructionoflhcbanditsupgrade