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

NNPDF Collaboration

doi:10.1140/epjc/s10052-021-09747-9

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

NNPDF Collaboration

(Astro)-Particles Physics

Research output: Contribution to Journal › Article › Academic › peer-review

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.

Original language	English
Article number	958
Pages (from-to)	1-12
Number of pages	12
Journal	The European Physical Journal C : Particles and Fields
Volume	81
Issue number	10
Early online date	30 Oct 2021
DOIs	https://doi.org/10.1140/epjc/s10052-021-09747-9
Publication status	Published - Oct 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).

Funding

S. C., S. F., J. C.-M., R. S. and C. S. are supported by the European Research Council under the European Union’s Horizon 2020 research and innovation Programme (grant agreement n.740006). M. U. and Z. K. are supported by the European Research Council under the European Union’s Horizon 2020 research and innovation Programme (grant agreement n.950246). M. U. and S. I. are partially supported by the Royal Society grant RGF/EA/180148. The work of M. U. is also funded by the Royal Society grant DH150088. The work of M. U., S. I., C. V. and Z. K. is partially supported by the STFC consolidated grant ST/L000385/1. The work of Z. K. was partly supported by supported by the European Research Council Consolidator Grant ”NNLOforLHC2”. J. R. is partially supported by NWO, the Dutch Research Council. C. V. is supported by the STFC grant ST/R504671/1. T. G. is supported by The Scottish Funding Council, grant H14027. R. L. P. and M. W. by the STFC grant ST/R504737/1. R. D. B., L. D. D. and E. R. N. are supported by the STFC grant ST/P000630/1. E. R. N. was also supported by the European Commission through the Marie Sklodowska-Curie Action ParDHonS (grant number 752748).

Funders	Funder number
European Commission
European Research Council
Scottish Funding Council	ST/P000630/1, H14027
UK Research and Innovation	ST/R504737/1
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	ST/R504671/1
Royal Society	DH150088, RGF/EA/180148
Horizon 2020 Framework Programme	950246, 752748, 740006
Science and Technology Facilities Council	ST/L000385/1

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This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 17 Partnerships for the Goals

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10.1140/epjc/s10052-021-09747-9

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Cite this

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title = "An open-source machine learning framework for global analyses of parton distributions",

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.",

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AU - Forte, Stefano

AU - Giani, Tommaso

AU - Iranipour, Shayan

AU - Kassabov, Zahari

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

Abstract

Bibliographical note

Funding

UN SDGs

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