Machine Learning tools for global PDF fits

Research output: Contribution to JournalArticleAcademic

Abstract

The use of machine learning algorithms in theoretical and experimental high-energy physics has experienced an impressive progress in recent years, with applications from trigger selection to jet substructure classification and detector simulation among many others. In this contribution, we review the machine learning tools used in the NNPDF family of global QCD analyses. These include multi-layer feed-forward neural networks for the model-independent parametrisation of parton distributions and fragmentation functions, genetic and covariance matrix adaptation algorithms for training and optimisation, and closure testing for the systematic validation of the fitting methodology.
Original languageEnglish
JournalarXiv.org
Publication statusPublished - 12 Sep 2018

Fingerprint

fragmentation
physics
matrix
methodology
simulation
energy
machine learning
detector
distribution
family

Bibliographical note

12 pages, 9 figures, to appear in the proceedings of the XXIIIth Quark Confinement and the Hadron Spectrum conference, 1-6 August 2018, University of Maynooth, Ireland

Keywords

  • hep-ph

Cite this

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title = "Machine Learning tools for global PDF fits",
abstract = "The use of machine learning algorithms in theoretical and experimental high-energy physics has experienced an impressive progress in recent years, with applications from trigger selection to jet substructure classification and detector simulation among many others. In this contribution, we review the machine learning tools used in the NNPDF family of global QCD analyses. These include multi-layer feed-forward neural networks for the model-independent parametrisation of parton distributions and fragmentation functions, genetic and covariance matrix adaptation algorithms for training and optimisation, and closure testing for the systematic validation of the fitting methodology.",
keywords = "hep-ph",
author = "Juan Rojo",
note = "12 pages, 9 figures, to appear in the proceedings of the XXIIIth Quark Confinement and the Hadron Spectrum conference, 1-6 August 2018, University of Maynooth, Ireland",
year = "2018",
month = "9",
day = "12",
language = "English",
journal = "arXiv.org",
issn = "2331-8422",

}

Machine Learning tools for global PDF fits. / Rojo, Juan.

In: arXiv.org, 12.09.2018.

Research output: Contribution to JournalArticleAcademic

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AB - The use of machine learning algorithms in theoretical and experimental high-energy physics has experienced an impressive progress in recent years, with applications from trigger selection to jet substructure classification and detector simulation among many others. In this contribution, we review the machine learning tools used in the NNPDF family of global QCD analyses. These include multi-layer feed-forward neural networks for the model-independent parametrisation of parton distributions and fragmentation functions, genetic and covariance matrix adaptation algorithms for training and optimisation, and closure testing for the systematic validation of the fitting methodology.

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