Speaker
Description
Extracting hadronic form factors from sparse and noisy lattice QCD data typically relies on parametric ansätze, introducing model-dependence. We present a generative framework based on denoising diffusion models for parameterisation-independent reconstruction of these quantities. The generative prior is built from a large ensemble of synthetic curves drawn from distinct functional classes rooted in different theoretical approaches to hadron structure. Applied to the proton gravitational form factors, the framework yields model-independent reconstructions consistent with lattice QCD, remaining robust even with only one or two conditioning points. The densely sampled posterior enables a direct extraction of the chiral low-energy constants c8 and c9, yielding the nucleon D-term.