Speaker
Description
Extracting parton distribution functions (PDFs) from Lattice QCD is crucial for understanding nucleon structure, but it fundamentally relies on solving a challenging ill-posed inverse problem. In this talk, I will present an overview of my work on addressing this problem within the pseudo-PDF framework. First, I will outline how Gaussian processes (GPs) provide flexible Bayesian priors that encode correlations and physical constraints without fixing a functional shape. Then, I will explain the different levels of inference from which one can obtain different types of reconstruction. Finally, I will show how to integrate all the models explored into a model selection/averaging procedure to obtain a more robust reconstruction of PDFs using different information criteria.