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

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Ball, Richard D. 
Carrazza, Stefano 
Cruz-Martinez, Juan 
Del Debbio, Luigi 
Forte, Stefano 

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.

Special Article - Tools for Experiment and Theory
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The European Physical Journal C
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Springer Berlin Heidelberg
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)