Generalized Parton Distributions: Phenomenology, Extraction, and Hadron Imaging

Abstract

Generalized Parton Distributions (GPDs) provide a framework for investigating the correlated momentum and spatial structure of quarks and gluons in hadrons and for accessing fundamental properties such as angular momentum and the QCD energy-momentum tensor. In this review, we discuss the present status of GPD phenomenology, emphasizing the challenges involved in connecting deeply virtual exclusive measurements to the underlying partonic structure. We organize this problem in terms of two successive inverse problems: the extraction of Compton Form Factors (CFFs) from measured observables and the reconstruction of GPDs from the convolution integrals defining the CFFs. We review the theoretical description and phenomenological parametrizations of GPDs, current strategies for CFF and GPD extraction, and the role of lattice QCD, Bayesian inference, uncertainty quantification, and artificial intelligence, including neural networks and interpretable machine-learning approaches. We discuss the limitations of present determinations and the opportunities offered by the Jefferson Lab program, complementary exclusive processes, and the future Electron-Ion Collider. Finally, we consider how increasingly precise and multidimensional information, together with new statistical and AI methodologies, will transform GPD phenomenology in the emerging era of precision hadron imaging.