Towards the compression of parton densities through machine learning algorithms

One of the most fascinating challenges in the context of parton density function (PDF) is the determination of the best combined PDF uncertainty from individual PDF sets. Since 2014 multiple methodologies have been developed to achieve this goal. In this proceedings we first summarize the strategy a...

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Detalles Bibliográficos
Autores principales: Carrazza, Stefano, Latorre, José I.
Lenguaje:eng
Publicado: 2016
Materias:
Particle Physics - Phenomenology
Acceso en línea:http://cds.cern.ch/record/2153473
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author Carrazza, Stefano
Latorre, José I.
author_facet Carrazza, Stefano
Latorre, José I.
author_sort Carrazza, Stefano
collection CERN
description One of the most fascinating challenges in the context of parton density function (PDF) is the determination of the best combined PDF uncertainty from individual PDF sets. Since 2014 multiple methodologies have been developed to achieve this goal. In this proceedings we first summarize the strategy adopted by the PDF4LHC15 recommendation and then, we discuss about a new approach to Monte Carlo PDF compression based on clustering through machine learning algorithms.
id cern-2153473
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2016
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spelling cern-21534732023-03-14T17:26:12Zhttp://cds.cern.ch/record/2153473engCarrazza, StefanoLatorre, José I.Towards the compression of parton densities through machine learning algorithmsParticle Physics - PhenomenologyOne of the most fascinating challenges in the context of parton density function (PDF) is the determination of the best combined PDF uncertainty from individual PDF sets. Since 2014 multiple methodologies have been developed to achieve this goal. In this proceedings we first summarize the strategy adopted by the PDF4LHC15 recommendation and then, we discuss about a new approach to Monte Carlo PDF compression based on clustering through machine learning algorithms.One of the most fascinating challenges in the context of parton density function (PDF) is the determination of the best combined PDF uncertainty from individual PDF sets. Since 2014 multiple methodologies have been developed to achieve this goal. In this proceedings we first summarize the strategy adopted by the PDF4LHC15 recommendation and then, we discuss about a new approach to Monte Carlo PDF compression based on clustering through machine learning algorithms.arXiv:1605.04345CERN-TH-2016-115CERN-TH-2016-115oai:cds.cern.ch:21534732016-05-13
spellingShingle Particle Physics - Phenomenology
Carrazza, Stefano
Latorre, José I.
Towards the compression of parton densities through machine learning algorithms
title Towards the compression of parton densities through machine learning algorithms
title_full Towards the compression of parton densities through machine learning algorithms
title_fullStr Towards the compression of parton densities through machine learning algorithms
title_full_unstemmed Towards the compression of parton densities through machine learning algorithms
title_short Towards the compression of parton densities through machine learning algorithms
title_sort towards the compression of parton densities through machine learning algorithms
topic Particle Physics - Phenomenology
url http://cds.cern.ch/record/2153473
work_keys_str_mv AT carrazzastefano towardsthecompressionofpartondensitiesthroughmachinelearningalgorithms
AT latorrejosei towardsthecompressionofpartondensitiesthroughmachinelearningalgorithms

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