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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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Lenguaje: | eng |
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2016
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Acceso en línea: | https://dx.doi.org/10.1051/epjconf/201612700006 http://cds.cern.ch/record/2205691 |
_version_ | 1780951573639200768 |
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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. |
id | cern-2205691 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2016 |
record_format | invenio |
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 |