Bayesian Inference and Gaussian Processes for PDF determination

by Tommaso Giani (Nikhef National institute for subatomic physics (NL))

Monday 4 Mar 2024, 14:00 → 16:00 Europe/Zurich

4/3-006 - TH Conference Room (CERN)

I will discuss a Bayesian methodology for the determination of parton distribution functions (PDFs). In this 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. I will discuss the general formalism, the Bayesian inference at the level of both parameters and hyperparameters, and the simplifications which occur when the observable entering the analysis is linear in the PDF. I will show a simple example where the new methodology is used to determine PDF from a set of Deep Inelastic Scattering data, and discuss how the proposed methodology allows for a well-defined statistical interpretation of the different sources of errors entering the PDF uncertainty.

cern24.pdf