Neural network approach to parton distributions fitting

We will show an application of neural networks to extract information on the structure of hadrons. A Monte Carlo over experimental data is performed to correctly reproduce data errors and correlations. A neural network is then trained on each Monte Carlo replica via a genetic algorithm. Results on t...

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Detalles Bibliográficos
Autores principales: Piccione, Andrea, Del Debbio, Luigi, Forte, Stefano, Latorre, Jose I., Rojo, Joan
Lenguaje:eng
Publicado: 2005
Materias:
Particle Physics - Phenomenology
Acceso en línea:https://dx.doi.org/10.1016/j.nima.2005.11.206
http://cds.cern.ch/record/882738
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author Piccione, Andrea
Del Debbio, Luigi
Forte, Stefano
Latorre, Jose I.
Rojo, Joan
author_facet Piccione, Andrea
Del Debbio, Luigi
Forte, Stefano
Latorre, Jose I.
Rojo, Joan
author_sort Piccione, Andrea
collection CERN
description We will show an application of neural networks to extract information on the structure of hadrons. A Monte Carlo over experimental data is performed to correctly reproduce data errors and correlations. A neural network is then trained on each Monte Carlo replica via a genetic algorithm. Results on the proton and deuteron structure functions, and on the nonsinglet parton distribution will be shown.
id cern-882738
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2005
record_format invenio
spelling cern-8827382023-03-14T20:25:35Zdoi:10.1016/j.nima.2005.11.206http://cds.cern.ch/record/882738engPiccione, AndreaDel Debbio, LuigiForte, StefanoLatorre, Jose I.Rojo, JoanNeural network approach to parton distributions fittingParticle Physics - PhenomenologyWe will show an application of neural networks to extract information on the structure of hadrons. A Monte Carlo over experimental data is performed to correctly reproduce data errors and correlations. A neural network is then trained on each Monte Carlo replica via a genetic algorithm. Results on the proton and deuteron structure functions, and on the nonsinglet parton distribution will be shown.We will show an application of neural networks to extract information on the structure of hadrons. A Monte Carlo over experimental data is performed to correctly reproduce data errors and correlations. A neural network is then trained on each Monte Carlo replica via a genetic algorithm. Results on the proton and deuteron structure functions, and on the nonsinglet parton distribution will be shown.hep-ph/0509067oai:cds.cern.ch:8827382005-09-07
spellingShingle Particle Physics - Phenomenology
Piccione, Andrea
Del Debbio, Luigi
Forte, Stefano
Latorre, Jose I.
Rojo, Joan
Neural network approach to parton distributions fitting
title Neural network approach to parton distributions fitting
title_full Neural network approach to parton distributions fitting
title_fullStr Neural network approach to parton distributions fitting
title_full_unstemmed Neural network approach to parton distributions fitting
title_short Neural network approach to parton distributions fitting
title_sort neural network approach to parton distributions fitting
topic Particle Physics - Phenomenology
url https://dx.doi.org/10.1016/j.nima.2005.11.206
http://cds.cern.ch/record/882738
work_keys_str_mv AT piccioneandrea neuralnetworkapproachtopartondistributionsfitting
AT deldebbioluigi neuralnetworkapproachtopartondistributionsfitting
AT fortestefano neuralnetworkapproachtopartondistributionsfitting
AT latorrejosei neuralnetworkapproachtopartondistributionsfitting
AT rojojoan neuralnetworkapproachtopartondistributionsfitting

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