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Title: An open-source machine learning framework for global analyses of parton distributions
Authors: 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
Issue Date: 1-Oct-2021
Publisher: Springer Science and Business Media Deutschland GmbH
Citation: 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 (2021-10-01). An open-source machine learning framework for global analyses of parton distributions. European Physical Journal C 81 (10) : 958. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
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. © 2021, The Author(s).
Source Title: European Physical Journal C
ISSN: 1434-6044
DOI: 10.1140/epjc/s10052-021-09747-9
Rights: Attribution 4.0 International
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