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

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

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

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

NNPDF Collaboration
, Stefano Carrazza
, Juan Cruz-Martinez
, Luigi Del Debbio
, Stefano Forte
, Tommaso Giani
, Shayan Iranipour
, Zahari Kassabov
, José I. Latorre
, Emanuele R. Nocera
, Rosalyn L. Pearson
, Juan Rojo
, Roy Stegeman
, Christopher Schwan
, Maria Ubiali^*
, Cameron Voisey
, Michael Wilson

^*Corresponding author for this work

School of Physics and Astronomy

Research output: Contribution to journal › Article › 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
Volume	81
Issue number	10
DOIs	https://doi.org/10.1140/epjc/s10052-021-09747-9
Publication status	Published - 30 Oct 2021

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10.1140/epjc/s10052-021-09747-9Licence: Creative Commons: Attribution (CC-BY)

Particle Theory at the Higgs Centre
Ball, R. (Principal Investigator), Boyle, P. (Co-investigator), Del Debbio, L. (Co-investigator), Gardi, E. (Co-investigator), Horsley, R. (Co-investigator), Kennedy, A. (Co-investigator), O'Connell, D. (Co-investigator), Smillie, J. (Co-investigator) & Zwicky, R. (Co-investigator)
STFC
1/10/17 → 30/09/21
Project: Research

NNPDF/nnpdf: Version 4.0.6
Zaharid (Creator), Cruz-Martinez, J. M. (Creator), Carrazza, S. (Creator), Pearson, R. (Creator), Voisey, C. (Creator), wilsonmr (Creator), Hartland, N. (Creator), Stegeman, R. (Creator), lucarottoli (Creator), Khalek, R. A. (Creator), Slade, E. (Creator), Nocera, E. R. (Creator), Barontini, A. (Creator), siranipour (Creator), tgiani (Creator), JesusUrtasun (Creator), mariaubiali (Creator), ldd69 (Creator), Candido, A. (Creator), Kalinkin, D. (Creator), gstagnit (Creator), Moore, J. (Creator), Costantini, M. N. (Creator) & Voltan, S. (Creator), Zenodo, 20 Jan 2023
DOI: 10.5281/zenodo.7554886, https://zenodo.org/record/7554886
Dataset
NNPDF/nnpdf: Version 4.0.5
Zaharid (Creator), Carrazza, S. (Creator), Cruz-Martinez, J. M. (Creator), Pearson, R. L. (Creator), Voisey, C. (Creator), wilsonmr (Creator), siranipour (Creator), Hartland, N. (Creator), Stegeman, R. (Creator), tgiani (Creator), lucarottoli (Creator), Khalek, R. A. (Creator), Slade, E. (Creator), Nocera, E. R. (Creator), Guffanti, A. (Creator), Barontini, A. (Creator), JesusUrtasun (Creator), mariaubiali (Creator), ldd69 (Creator), Kalinkin, D. (Creator), gstagnit (Creator) & Moore, J. (Creator), Zenodo, 12 May 2022
DOI: 10.5281/zenodo.6542572, https://zenodo.org/record/6542572
Dataset

Cite this

NNPDF Collaboration, Carrazza, S., Cruz-Martinez, J., Del Debbio, L., Forte, S., Giani, T., Iranipour, S., Kassabov, Z., Latorre, J. I., Nocera, E. R., Pearson, R. L., Rojo, J., Stegeman, R., Schwan, C., Ubiali, M., Voisey, C., & Wilson, M. (2021). An open-source machine learning framework for global analyses of parton distributions. The European Physical Journal C, 81(10), 1-12. Article 958. https://doi.org/10.1140/epjc/s10052-021-09747-9

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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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year = "2021",

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doi = "10.1140/epjc/s10052-021-09747-9",

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NNPDF Collaboration, Carrazza, S, Cruz-Martinez, J, Del Debbio, L, Forte, S, Giani, T, Iranipour, S, Kassabov, Z, Latorre, JI, Nocera, ER, Pearson, RL, Rojo, J, Stegeman, R, Schwan, C, Ubiali, M, Voisey, C & Wilson, M 2021, 'An open-source machine learning framework for global analyses of parton distributions', The European Physical Journal C, vol. 81, no. 10, 958, pp. 1-12. https://doi.org/10.1140/epjc/s10052-021-09747-9

TY - JOUR

T1 - An open-source machine learning framework for global analyses of parton distributions

AU - NNPDF Collaboration

AU - Ball, Richard D.

AU - Carrazza, Stefano

AU - Cruz-Martinez, Juan

AU - Del Debbio, Luigi

AU - Forte, Stefano

AU - Giani, Tommaso

AU - Iranipour, Shayan

AU - Kassabov, Zahari

AU - Latorre, José I.

AU - Nocera, Emanuele R.

AU - Pearson, Rosalyn L.

AU - Rojo, Juan

AU - Stegeman, Roy

AU - Schwan, Christopher

AU - Ubiali, Maria

AU - Voisey, Cameron

AU - Wilson, Michael

PY - 2021/10/30

Y1 - 2021/10/30

N2 - 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.

AB - 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.

U2 - 10.1140/epjc/s10052-021-09747-9

DO - 10.1140/epjc/s10052-021-09747-9

M3 - Article

SN - 1434-6044

VL - 81

SP - 1

EP - 12

JO - The European Physical Journal C

JF - The European Physical Journal C

IS - 10

M1 - 958

ER -

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

Abstract

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Projects

Particle Theory at the Higgs Centre

Datasets

NNPDF/nnpdf: Version 4.0.6

NNPDF/nnpdf: Version 4.0.5

Cite this