Machine Learning tools for global PDF fits

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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
EventXXIIIth Quark Confinement and the Hadron Spectrum conference: 1-6 August 2018, University of Maynooth, Ireland - Maynooth, Ireland
Duration: 1 Aug 20186 Aug 2018
Conference number: 23th

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

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