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dc.contributor.authorBall, Richard D.
dc.contributor.authorCarrazza, Stefano
dc.contributor.authorCruz-Martinez, Juan
dc.contributor.authorDel Debbio, Luigi
dc.contributor.authorForte, Stefano
dc.contributor.authorGiani, Tommaso
dc.contributor.authorIranipour, Shayan
dc.contributor.authorKassabov, Zahari
dc.contributor.authorLatorre, Jose I.
dc.contributor.authorNocera, Emanuele R.
dc.contributor.authorPearson, Rosalyn L.
dc.contributor.authorRojo, Juan
dc.contributor.authorStegeman, Roy
dc.contributor.authorSchwan, Christopher
dc.contributor.authorUbiali, Maria
dc.contributor.authorVoisey, Cameron
dc.contributor.authorWilson, Michael
dc.date.accessioned2021-10-31T16:21:46Z
dc.date.available2021-10-31T16:21:46Z
dc.date.issued2021-10-30
dc.date.submitted2021-09-17
dc.identifier.issn1434-6044
dc.identifier.others10052-021-09747-9
dc.identifier.other9747
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330122
dc.description.abstractAbstract: 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.
dc.languageen
dc.publisherSpringer Berlin Heidelberg
dc.subjectSpecial Article - Tools for Experiment and Theory
dc.titleAn open-source machine learning framework for global analyses of parton distributions
dc.typeArticle
dc.date.updated2021-10-31T16:21:46Z
prism.issueIdentifier10
prism.publicationNameThe European Physical Journal C
prism.volume81
dc.identifier.doi10.17863/CAM.77567
dcterms.dateAccepted2021-10-10
rioxxterms.versionofrecord10.1140/epjc/s10052-021-09747-9
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn1434-6052
pubs.funder-project-idScience and Technology Facilities Council (ST/L000385/1, ST/P000630/1.)
pubs.funder-project-idScience and Technology Facilities Council (ST/R504671/1, T/R504737/1)
pubs.funder-project-idScottish Funding Council (H14027)
pubs.funder-project-idMarie Sklodowska-Curie Actions (752748)
pubs.funder-project-idH2020 European Research Council (740006, 950246)
pubs.funder-project-idH2020 European Research Council (NNLOforLHC2)
pubs.funder-project-idRoyal Society (DH150088, RGF/EA/180148)
dc.identifier.arxiv2109.02671


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