Year: 2026
Type: preprint
Abstract: We introduce a nonparametric pixel-based framework for the Bayesian inference and imaging of transverse momentum dependent (TMD) parton distributions. The methodology integrates TMD evolution within the Collins-Soper-Sterman formalism in a differentiable framework, and leverages generative AI through a hybrid normalizing flow-driven Metropolis-Hastings algorithm for efficient posterior sampling. The framework is validated through multi-scale closure tests of increasing complexity. Using singular value decomposition, we characterize the existence of null TMDs, functional components that remain unconstrained by observables, and demonstrate how multi-scale data break these degeneracies, enabling 3D partonic imaging.
Source: arXiv (Cornell University)
Author: Zaccheddu, Marco
Institution: Thomas Jefferson National Accelerator Facility
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: G2562462929, G4531151473
OpenAlex ID: W7208777613