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Inclusive tagging of B-flavour at LHCb [Vidyo]
<!--HTML-->One of the most important procedure needed for the study of CP violation in Beauty sector is the tagging of the flavour of neutral B-mesons at production. The harsh environment of the Large Hadron Collider makes it particularly hard to succeed in this task. We present a proposal to...
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Lenguaje: | eng |
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2017
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Acceso en línea: | http://cds.cern.ch/record/2256697 |
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author | Rogozhnikov, Aleksei |
author_facet | Rogozhnikov, Aleksei |
author_sort | Rogozhnikov, Aleksei |
collection | CERN |
description | <!--HTML-->One of the most important procedure needed for the study of CP violation in Beauty sector is the tagging of the flavour of neutral B-mesons at production. The harsh environment of the Large Hadron Collider makes it particularly hard to succeed in this task. We present a proposal to upgrade current flavour tagging strategy in LHCb experiment. This strategy consists of inclusive tagging ensemble methods (i.e: the use inclusive information about the event without a firm selection rule), which are combined using a probabilistic model for each event. The probabilistic model uses all reconstructed tracks and secondary vertices to obtain well-determined probability of B flavour at production. Such approach reduces the dependence on the performance of lower level identification capacities and thus has the potential to increase the overall performance. |
id | cern-2256697 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2017 |
record_format | invenio |
spelling | cern-22566972022-11-02T22:34:07Zhttp://cds.cern.ch/record/2256697engRogozhnikov, AlekseiInclusive tagging of B-flavour at LHCb [Vidyo]IML Machine Learning WorkshopMachine Learning<!--HTML-->One of the most important procedure needed for the study of CP violation in Beauty sector is the tagging of the flavour of neutral B-mesons at production. The harsh environment of the Large Hadron Collider makes it particularly hard to succeed in this task. We present a proposal to upgrade current flavour tagging strategy in LHCb experiment. This strategy consists of inclusive tagging ensemble methods (i.e: the use inclusive information about the event without a firm selection rule), which are combined using a probabilistic model for each event. The probabilistic model uses all reconstructed tracks and secondary vertices to obtain well-determined probability of B flavour at production. Such approach reduces the dependence on the performance of lower level identification capacities and thus has the potential to increase the overall performance.oai:cds.cern.ch:22566972017 |
spellingShingle | Machine Learning Rogozhnikov, Aleksei Inclusive tagging of B-flavour at LHCb [Vidyo] |
title | Inclusive tagging of B-flavour at LHCb [Vidyo] |
title_full | Inclusive tagging of B-flavour at LHCb [Vidyo] |
title_fullStr | Inclusive tagging of B-flavour at LHCb [Vidyo] |
title_full_unstemmed | Inclusive tagging of B-flavour at LHCb [Vidyo] |
title_short | Inclusive tagging of B-flavour at LHCb [Vidyo] |
title_sort | inclusive tagging of b-flavour at lhcb [vidyo] |
topic | Machine Learning |
url | http://cds.cern.ch/record/2256697 |
work_keys_str_mv | AT rogozhnikovaleksei inclusivetaggingofbflavouratlhcbvidyo AT rogozhnikovaleksei imlmachinelearningworkshop |