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

The NNPDF collaboration determines the structure of the proton using Machine Learning methods. This is the main repository of the fitting and analysis frameworks. In particular it contains all the necessary tools to reproduce the NNPDF4.0 PDF determinations.

Documentation

The documentation is available at https://docs.nnpdf.science/

Install

See the NNPDF installation guide for instructions on how to install and use the code, using either conda or pip, requirements and dependencies As a first step we recommend to follow one of the tutorials.

While we aim to keep the tip of the master branch always stable, tested and correct, runs of the code intended for publication should use one the released versions.

Cite

This code is described in the following paper:

@article{NNPDF:2021uiq,
    author = "Ball, Richard D. and others",
    collaboration = "NNPDF",
    title = "{An open-source machine learning framework for global analyses of parton distributions}",
    eprint = "2109.02671",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    reportNumber = "Edinburgh 2021/13, Nikhef-2021-020, TIF-UNIMI-2021-12",
    doi = "10.1140/epjc/s10052-021-09747-9",
    journal = "Eur. Phys. J. C",
    volume = "81",
    number = "10",
    pages = "958",
    year = "2021"
}

If you use the code to produce new results in a scientific publication, we ask you to please cite this paper, the zenodo entry and follow the Citation Policy.

Contribute

We welcome bug reports or feature requests sent to the issue tracker. You may use the issue tracker for help and questions as well.

If you would like contribute to the code, please follow the Contribution Guidelines.

When developing locally you can test your changes with pytest, running from the root of the repository:

  pytest --mpl --pyargs n3fit validphys

License

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License version 3 as published by the Free Software Foundation.

© Copyright 2021-2025, the NNPDF collaboration