Nuclear parton distributions from neural networks

Rabah Abdul Khalek; Jacob J. Ethier; Juan Rojo

doi:10.5506/APhysPolBSupp.12.927

Nuclear parton distributions from neural networks

Rabah Abdul Khalek, Jacob J. Ethier, Juan Rojo

(Astro)-Particles Physics

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Abstract

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 language	English
Pages (from-to)	927-933
Number of pages	7
Journal	Acta Physica Polonica B, Proceedings Supplement
Volume	12
Issue number	4
Early online date	14 Nov 2018
DOIs	https://doi.org/10.5506/APhysPolBSupp.12.927
Publication status	Published - 2019

Funding

This research has been supported by a European Research Council Starting grant “PDF4BSM”, and by the Netherlands Organization for Scientific Research (NWO).

Funders	Funder number
Netherlands Organization for Scientific Research
European Research Council	PDF4BSM
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

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hep-ph

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10.5506/APhysPolBSupp.12.927

1811.05858v1

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Nuclear parton distributions from neural networks

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