An open-source machine learning framework for global analyses of parton distributions

Ball, Richard D.; Carrazza, Stefano; Cruz-Martinez, Juan; Del Debbio, Luigi; Forte, Stefano; Giani, Tommaso; Iranipour, Shayan; Kassabov, Zahari; Latorre, Jose I.; Nocera, Emanuele R.; Pearson, Rosalyn L.; Rojo, Juan; Stegeman, Roy; Schwan, Christopher; Ubiali, Maria; Voisey, Cameron; Wilson, Michael

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Authors

Ball, Richard D.

Carrazza, Stefano

Cruz-Martinez, Juan

Del Debbio, Luigi

Forte, Stefano

Giani, Tommaso

Iranipour, Shayan

Publication Date

2021-10-30

Journal Title

The European Physical Journal C

ISSN

1434-6044

Publisher

Springer Berlin Heidelberg

Volume

Issue

Language

Type

Article

This Version

VoR

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Citation

Ball, R. D., Carrazza, S., Cruz-Martinez, J., Del Debbio, L., Forte, S., Giani, T., Iranipour, S., et al. (2021). An open-source machine learning framework for global analyses of parton distributions. The European Physical Journal C, 81 (10) https://doi.org/10.1140/epjc/s10052-021-09747-9

Abstract

Abstract: We present the software framework underlying the NNPDF4.0 global determination of parton distribution functions (PDFs). The code is released under an open source licence and is accompanied by extensive documentation and examples. The code base is composed by a PDF fitting package, tools to handle experimental data and to efficiently compare it to theoretical predictions, and a versatile analysis framework. In addition to ensuring the reproducibility of the NNPDF4.0 (and subsequent) determination, the public release of the NNPDF fitting framework enables a number of phenomenological applications and the production of PDF fits under user-defined data and theory assumptions.

Keywords

Special Article - Tools for Experiment and Theory

Sponsorship

Science and Technology Facilities Council (ST/L000385/1, ST/P000630/1.)

Science and Technology Facilities Council (ST/R504671/1, T/R504737/1)

Scottish Funding Council (H14027)

Marie Sklodowska-Curie Actions (752748)

H2020 European Research Council (740006, 950246)

H2020 European Research Council (NNLOforLHC2)

Royal Society (DH150088, RGF/EA/180148)

Identifiers

s10052-021-09747-9, 9747

External DOI: https://doi.org/10.1140/epjc/s10052-021-09747-9

This record's URL: https://www.repository.cam.ac.uk/handle/1810/331714

Rights

Licence:

http://creativecommons.org/licenses/by/4.0/

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