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arXiv 2109.02671hep-ph
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An open-source machine learning framework for global analyses of parton distributions

Richard D. Ball, Stefano Carrazza, Juan Cruz-Martinez, Luigi Del Debbio, Stefano Forte, Tommaso Giani, Shayan Iranipour, Zahari Kassabov, Jose I. Latorre, Emanu… 展开作者

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

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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