4.7 Article

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

期刊

EUROPEAN PHYSICAL JOURNAL C
卷 81, 期 10, 页码 -

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SPRINGER
DOI: 10.1140/epjc/s10052-021-09747-9

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资金

  1. European Research Council under the European Union's Horizon 2020 research and innovation Programme [740006, 950246]
  2. Royal Society [RGF/EA/180148, DH150088]
  3. STFC consolidated grant [ST/L000385/1]
  4. European Research Council Consolidator Grant NNLOforLHC2
  5. NWO, the Dutch Research Council
  6. STFC [ST/R504671/1, ST/R504737/1, ST/P000630/1]
  7. Scottish Funding Council [H14027]
  8. European Commission through the Marie Sklodowska-Curie Action ParDHonS [752748]
  9. Marie Curie Actions (MSCA) [752748] Funding Source: Marie Curie Actions (MSCA)

向作者/读者索取更多资源

The software framework for the NNPDF4.0 global determination of parton distribution functions (PDFs) is released under an open source license, providing extensive documentation and examples for users. The framework includes tools for PDF fitting, handling experimental data, comparing it to theoretical predictions, and conducting versatile analysis. The public release of the NNPDF fitting framework ensures reproducibility of the NNPDF4.0 determination and enables various phenomenological applications and PDF fits based on user-defined data and theory assumptions.
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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