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.

The path to proton structure at 1% accuracy / R.D. Ball, S. Carrazza, C. Juan, L. Del Debbio, S. Forte, T. Giani, S. Iranipour, Z. Kassabov, J.I. Latorre, E.R. Nocera, R.L. Pearson, J. Rojo, R. Stegeman, C. Schwan, M. Ubiali, C. Voisey, M. Wilson. - In: THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS. - ISSN 1434-6044. - 82:5(2022), pp. 428.1-428.119. [10.1140/epjc/s10052-022-10328-7]

The path to proton structure at 1% accuracy

S. Carrazza
Secondo
;
C. Juan;S. Forte
;
R. Stegeman;C. Schwan;
2022

Abstract

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.
Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici
Settore FIS/04 - Fisica Nucleare e Subnucleare
H2020_ERC17SFORT_01 - Proton strucure for discovery at the Large Hadron Collider (NNNPDF) - FORTE, STEFANO - H2020_ERC - Horizon 2020_Europern Research Council - 2017
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/929864
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