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ML Techniques for heavy flavour identification in CMS

<!--HTML-->Jet flavour identification is a fundamental component for the physics program of the LHC-based experiments. The presence of multiple flavours to be identified leads to a multiclass classification problem. Moreover, the classification of boosted jets has acquired an increasing import...

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Autor principal: Bols, Emil Sorensen
Lenguaje:eng
Publicado: 2019
Materias:
Acceso en línea:http://cds.cern.ch/record/2672563
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author Bols, Emil Sorensen
author_facet Bols, Emil Sorensen
author_sort Bols, Emil Sorensen
collection CERN
description <!--HTML-->Jet flavour identification is a fundamental component for the physics program of the LHC-based experiments. The presence of multiple flavours to be identified leads to a multiclass classification problem. Moreover, the classification of boosted jets has acquired an increasing importance in the physics program of CMS. In this presentation we will present the performance on both simulated and real data of our latest resolved and boosted heavy flavour taggers as well as the future prospects for the evolution of these techniques and the technical strategies adopted to deploy them in the harsh computing environment of a large-scale HEP computing software stack.
id cern-2672563
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2019
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spelling cern-26725632022-11-02T22:33:36Zhttp://cds.cern.ch/record/2672563engBols, Emil SorensenML Techniques for heavy flavour identification in CMS3rd IML Machine Learning WorkshopLPCC Workshops<!--HTML-->Jet flavour identification is a fundamental component for the physics program of the LHC-based experiments. The presence of multiple flavours to be identified leads to a multiclass classification problem. Moreover, the classification of boosted jets has acquired an increasing importance in the physics program of CMS. In this presentation we will present the performance on both simulated and real data of our latest resolved and boosted heavy flavour taggers as well as the future prospects for the evolution of these techniques and the technical strategies adopted to deploy them in the harsh computing environment of a large-scale HEP computing software stack.oai:cds.cern.ch:26725632019
spellingShingle LPCC Workshops
Bols, Emil Sorensen
ML Techniques for heavy flavour identification in CMS
title ML Techniques for heavy flavour identification in CMS
title_full ML Techniques for heavy flavour identification in CMS
title_fullStr ML Techniques for heavy flavour identification in CMS
title_full_unstemmed ML Techniques for heavy flavour identification in CMS
title_short ML Techniques for heavy flavour identification in CMS
title_sort ml techniques for heavy flavour identification in cms
topic LPCC Workshops
url http://cds.cern.ch/record/2672563
work_keys_str_mv AT bolsemilsorensen mltechniquesforheavyflavouridentificationincms
AT bolsemilsorensen 3rdimlmachinelearningworkshop