Nuclear Parton Distributions from Neural Networks

Research output: Contribution to JournalArticleAcademic

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 a 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
JournalarXiv.org
Publication statusPublished - 14 Nov 2018

Fingerprint

partons
distribution functions
methodology
atomic weights
neutral currents
closures
leptons
education
nuclei
scattering

Bibliographical note

8 pages, 3 figures, to appear in the proceedings of Diffraction and Low-x 2018

Keywords

  • hep-ph

Cite this

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title = "Nuclear Parton Distributions from Neural Networks",
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 a 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.",
keywords = "hep-ph",
author = "Khalek, {Rabah Abdul} and Ethier, {Jacob J.} and Juan Rojo",
note = "8 pages, 3 figures, to appear in the proceedings of Diffraction and Low-x 2018",
year = "2018",
month = "11",
day = "14",
language = "English",
journal = "arXiv.org",
issn = "2331-8422",

}

Nuclear Parton Distributions from Neural Networks. / Khalek, Rabah Abdul; Ethier, Jacob J.; Rojo, Juan.

In: arXiv.org, 14.11.2018.

Research output: Contribution to JournalArticleAcademic

TY - JOUR

T1 - Nuclear Parton Distributions from Neural Networks

AU - Khalek, Rabah Abdul

AU - Ethier, Jacob J.

AU - Rojo, Juan

N1 - 8 pages, 3 figures, to appear in the proceedings of Diffraction and Low-x 2018

PY - 2018/11/14

Y1 - 2018/11/14

N2 - 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 a 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.

AB - 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 a 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.

KW - hep-ph

M3 - Article

JO - arXiv.org

JF - arXiv.org

SN - 2331-8422

ER -