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.
European Physical Journal C
- Pub Date:
- May 2022
- High Energy Physics - Phenomenology;
- High Energy Physics - Experiment
- 139 pages, more than 60 figures. A companion paper describes the open-source NNPDF code. Figs 9.1 and 9.3 replaced after correction of a plotting bug (thanks to J.Huston and G.Salam for spotting it). A few more typos corrected and a reference updated. Final published version. References updated, several typos corrected, 72 issues raised by the referee addressed