Reconstruction of Gravitational Form Factors using Generative Machine Learning
This paper introduces a generative framework based on denoising diffusion to perform model-independent, non-parametric reconstruction of proton gravitational form factors from sparse and noisy data, enabling robust extraction of chiral low-energy constants and the nucleon D-term across the full kinematic range.