The path to proton structure at 1% accuracy NNPDF Collaboration

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.

Automated summary

We present NNPDF4.0, a new set of parton distribution functions (PDFs) based on a global dataset and machine learning techniques. The methodology includes the expansion of the dataset, optimization of hyperparameters, and the implementation of theoretical improvements. The results have been validated through closure tests and analyzed for their dependence on input choices. The implications of NNPDF4.0 on LHC processes have also been studied.