Minimisation strategies for the determination of parton density functions

We discuss the current minimisation strategies adopted by research projects involving the determination of parton distribution functions (PDFs) and fragmentation functions (FFs) through the training of neural networks. We present a short overview of a proton PDF determination obtained using the cova...

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Detalles Bibliográficos
Autores principales: Carrazza, Stefano, Hartland, Nathan P.
Lenguaje:eng
Publicado: 2017
Materias:
hep-ph
Particle Physics - Phenomenology
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/1085/5/052007
http://cds.cern.ch/record/2300080
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author Carrazza, Stefano
Hartland, Nathan P.
author_facet Carrazza, Stefano
Hartland, Nathan P.
author_sort Carrazza, Stefano
collection CERN
description We discuss the current minimisation strategies adopted by research projects involving the determination of parton distribution functions (PDFs) and fragmentation functions (FFs) through the training of neural networks. We present a short overview of a proton PDF determination obtained using the covariance matrix adaptation evolution strategy (CMA-ES) optimisation algorithm. We perform comparisons between the CMA-ES and the standard nodal genetic algorithm (NGA) adopted by the NNPDF collaboration.
id oai-inspirehep.net-1639278
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2017
record_format invenio
spelling oai-inspirehep.net-16392782021-05-03T20:33:32Zdoi:10.1088/1742-6596/1085/5/052007http://cds.cern.ch/record/2300080engCarrazza, StefanoHartland, Nathan P.Minimisation strategies for the determination of parton density functionshep-phParticle Physics - PhenomenologyParticle Physics - PhenomenologyWe discuss the current minimisation strategies adopted by research projects involving the determination of parton distribution functions (PDFs) and fragmentation functions (FFs) through the training of neural networks. We present a short overview of a proton PDF determination obtained using the covariance matrix adaptation evolution strategy (CMA-ES) optimisation algorithm. We perform comparisons between the CMA-ES and the standard nodal genetic algorithm (NGA) adopted by the NNPDF collaboration.arXiv:1711.09991CERN-TH-2017-241oai:inspirehep.net:16392782017-11-27
spellingShingle hep-ph
Particle Physics - Phenomenology
Particle Physics - Phenomenology
Carrazza, Stefano
Hartland, Nathan P.
Minimisation strategies for the determination of parton density functions
title Minimisation strategies for the determination of parton density functions
title_full Minimisation strategies for the determination of parton density functions
title_fullStr Minimisation strategies for the determination of parton density functions
title_full_unstemmed Minimisation strategies for the determination of parton density functions
title_short Minimisation strategies for the determination of parton density functions
title_sort minimisation strategies for the determination of parton density functions
topic hep-ph
Particle Physics - Phenomenology
Particle Physics - Phenomenology
url https://dx.doi.org/10.1088/1742-6596/1085/5/052007
http://cds.cern.ch/record/2300080
work_keys_str_mv AT carrazzastefano minimisationstrategiesforthedeterminationofpartondensityfunctions
AT hartlandnathanp minimisationstrategiesforthedeterminationofpartondensityfunctions

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