The path to proton structure at 1% accuracy
Authors
Ball, Richard D
Carrazza, Stefano
Del Debbio, Luigi
Giani, Tommaso
Iranipour, Shayan
Kassabov, Zahari
Latorre, Jose I
Pearson, Rosalyn L
Rojo, Juan
Stegeman, Roy
Voisey, Cameron
Wilson, Michael
Publication Date
2022-05Journal Title
The European Physical Journal C
ISSN
1434-6044
Publisher
Springer Science and Business Media LLC
Volume
82
Issue
5
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Ball, R. D., Carrazza, S., Cruz-Martinez, J., Del Debbio, L., Forte, S., Giani, T., Iranipour, S., et al. (2022). The path to proton structure at 1% accuracy. The European Physical Journal C, 82 (5) https://doi.org/10.1140/epjc/s10052-022-10328-7
Abstract
<jats:title>Abstract</jats:title><jats:p>We present a new set of parton distribution functions (PDFs) based on a fully global dataset and machine learning techniques: NNPDF4.0. We expand the NNPDF3.1 determination with 44 new datasets, mostly from the LHC. We derive a novel methodology through hyperparameter optimization, leading to an efficient fitting algorithm built upon stochastic gradient descent. We use NNLO QCD calculations and account for NLO electroweak corrections and nuclear uncertainties. Theoretical improvements in the PDF description include a systematic implementation of positivity constraints and integrability of sum rules. We validate our methodology by means of closure tests and “future tests” (i.e. tests of backward and forward data compatibility), and assess its stability, specifically upon changes of PDF parametrization basis. We study the internal compatibility of our dataset, and investigate the dependence of results both upon the choice of input dataset and of fitting methodology. We perform a first study of the phenomenological implications of NNPDF4.0 on representative LHC processes. The software framework used to produce NNPDF4.0 is made available as an open-source package together with documentation and examples.</jats:p>
Keywords
Regular Article - Theoretical Physics
Sponsorship
Science and Technology Facilities Council (ST/L000385/1)
European Research Council (683211)
Science and Technology Facilities Council (ST/R504671/1)
Identifiers
s10052-022-10328-7, 10328
External DOI: https://doi.org/10.1140/epjc/s10052-022-10328-7
This record's URL: https://www.repository.cam.ac.uk/handle/1810/337025
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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