Normalizing Flows to Reconstruct Pseudo-PDFs
High Energy Physics - Lattice
2026-07-28 v1 Machine Learning
High Energy Physics - Phenomenology
Abstract
We investigate a normalizing-flow approach for reconstructing parton distribution functions (PDFs) from synthetic matrix-element data. Our framework combines Gaussian Process priors with invertible neural networks to learn a posterior distribution over PDFs consistent with limited Ioffe-time data. We demonstrate that the architecture preserves physical constraints and extrapolation properties.
Cite
@article{arxiv.2607.25282,
title = {Normalizing Flows to Reconstruct Pseudo-PDFs},
author = {Yamil Cahuana Medrano and Kostas Orginos},
journal= {arXiv preprint arXiv:2607.25282},
year = {2026}
}