Nuclear parton distributions from neural networks

Rabah Abdul Khalek, Jacob J. Ethier, Juan Rojo

Research output: Contribution to JournalArticleAcademicpeer-review

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In this contribution, we present a status report on the recent progress towards an analysis of nuclear parton distribution functions (nPDFs) using the NNPDF methodology. We discuss how the NNPDF fitting approach can be extended to account for the dependence on the atomic mass number A, and introduce novel algorithms to improve the training of the neural network parameters within the NNPDF framework. Finally, we present preliminary results of an nPDF fit to neutral current deep-inelastic lepton–nucleus scattering data, and demonstrate how one can validate the new fitting methodology by means of closure tests.

Original languageEnglish
Pages (from-to)927-933
Number of pages7
JournalActa Physica Polonica B, Proceedings Supplement
Issue number4
Early online date14 Nov 2018
Publication statusPublished - 2019


  • hep-ph


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