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Flavor Tagging with Deep Neural Networks at Belle II
<!--HTML-->The Belle II experiment is mainly designed to investigate the decay of B meson pairs from $\Upsilon(4S)$ decays, produced by the asymmetric electron-positron collider SuperKEKB. The determination of the B meson flavor, so-called flavor tagging, plays an important role in analyses a...
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Lenguaje: | eng |
Publicado: |
2017
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Acceso en línea: | http://cds.cern.ch/record/2256688 |
_version_ | 1780953749183791104 |
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author | Gemmler, Jochen |
author_facet | Gemmler, Jochen |
author_sort | Gemmler, Jochen |
collection | CERN |
description | <!--HTML-->The Belle II experiment is mainly designed to investigate the decay of B meson pairs from $\Upsilon(4S)$ decays, produced by the asymmetric electron-positron collider SuperKEKB. The determination of the B meson flavor, so-called flavor tagging, plays an important role in analyses and can be inferred in many cases directly from the final state particles. In this talk a successful approach of B meson flavor tagging utilizing a Deep Neural Network is presented. Monte Carlo studies show a significant improvement with respect to the established category-based flavor tagging algorithm. |
id | cern-2256688 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2017 |
record_format | invenio |
spelling | cern-22566882022-11-02T22:34:07Zhttp://cds.cern.ch/record/2256688engGemmler, JochenFlavor Tagging with Deep Neural Networks at Belle IIIML Machine Learning WorkshopMachine Learning<!--HTML-->The Belle II experiment is mainly designed to investigate the decay of B meson pairs from $\Upsilon(4S)$ decays, produced by the asymmetric electron-positron collider SuperKEKB. The determination of the B meson flavor, so-called flavor tagging, plays an important role in analyses and can be inferred in many cases directly from the final state particles. In this talk a successful approach of B meson flavor tagging utilizing a Deep Neural Network is presented. Monte Carlo studies show a significant improvement with respect to the established category-based flavor tagging algorithm.oai:cds.cern.ch:22566882017 |
spellingShingle | Machine Learning Gemmler, Jochen Flavor Tagging with Deep Neural Networks at Belle II |
title | Flavor Tagging with Deep Neural Networks at Belle II |
title_full | Flavor Tagging with Deep Neural Networks at Belle II |
title_fullStr | Flavor Tagging with Deep Neural Networks at Belle II |
title_full_unstemmed | Flavor Tagging with Deep Neural Networks at Belle II |
title_short | Flavor Tagging with Deep Neural Networks at Belle II |
title_sort | flavor tagging with deep neural networks at belle ii |
topic | Machine Learning |
url | http://cds.cern.ch/record/2256688 |
work_keys_str_mv | AT gemmlerjochen flavortaggingwithdeepneuralnetworksatbelleii AT gemmlerjochen imlmachinelearningworkshop |