Apr 8 – 12, 2024
Maison MINATEC, Grenoble, FRANCE
Europe/Paris timezone

Bayesian Inference and Gaussian Processes for PDF determination

Apr 10, 2024, 9:50 AM
Maison MINATEC, Grenoble, FRANCE

Maison MINATEC, Grenoble, FRANCE

3 Parv. Louis Néel, 38054 Grenoble
Regular parallel talk WG1: Structure Functions and Parton Densities WG1


Tommaso Giani (Nikhef)


We discuss a Bayesian methodology for the solution of the inverse problem underlying the determination of parton distribution functions (PDFs). In our approach, Gaussian Processes (GPs) are used to model the PDF prior, while Bayes’ theorem is used in order to determine the posterior distribution of the PDFs given a set of data. We discuss the general formalism, the Bayesian inference at the level of both parameters and hyperpa- rameters, and the simplifications which occur when the observable entering the analysis is linear in the PDF. We benchmark the new methodology in two simple examples for the determination of a single PDF flavor from a set of Deep Inelastic Scattering (DIS) data and from a set of equal-time correlators computed using lattice QCD. We discuss our results, showing how the proposed methodology allows for a well-defined statistical interpretation of the different sources of errors entering the PDF uncertainty, and how results can be validated a posteriori.

Primary authors

Dr Alessandro Candido (Cern) Dr Giacomo Petrillo (Universita' di Firenze) Prof. Luigi Del Debbio (University of Edinburgh) Tommaso Giani (Nikhef)

Presentation materials