NNPDF4.0

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 optimisation, 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.

Paper: https://arxiv.org/abs/2109.02653

Run cards: https://github.com/NNPDF/nnpdf/tree/master/n3fit/runcards/reproduce_nnpdf40/

Representative training reports: https://data.nnpdf.science/nnpdf40-reports/

The full NNPDF code is available open source at https://nnpdf.mi.infn.it/nnpdf-open-source-code/

The PDF sets are available below and at the LHAPDF website:

The following PDF sets have been used in studies in the NNPDF4.0 paper and can only be downloaded here, not from the LHAPDF website:

NNPDF4.0

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