Speaker
Eva Groenendijk
(University of Milan and INFN Milan)
Description
Parton distribution functions (PDFs) are a key ingredient for precision predictions at particle colliders, describing the dynamics of quarks and gluons inside the proton. Their determination is a challenging inverse problem, where PDFs are extracted from data via a convolution with theoretical predictions. The NNPDF methodology uses a neural network as a flexible parameterisation and a Monte Carlo replica method for a faithful representation of uncertainties. In the new NNPDF release, we introduce a novel hyperoptimisation to determine the set of hyperparameters used in the fit. The main novelty is the ability to determine the uncertainty associated with the hyperparameter choice, along with an optimal set of parameters.
Author
Eva Groenendijk
(University of Milan and INFN Milan)