Speaker
Description
Generalized parton distributions (GPDs) encode the three-dimensional partonic structure of the nucleon and are inferred from exclusive scattering experiments such as deeply virtual Compton scattering. The reconstruction is an underdetermined inverse problem: the measurements — Compton form factors — constrain only one region of the underlying distribution, leaving an unmeasured complementary domain that the reconstruction must still populate. We present a neural-field framework that parametrizes the underlying double distribution F(β,α), a parametrization that links the measured DGLAP region (|x|>ξ) to the unmeasured ERBL region (|x|<ξ), with a Fourier-feature MLP and propagates it to the observable via the Radon transform. Closure tests against the Goloskokov–Kroll model recover the measured-region GPD across a range of skewness ξ; the unmeasured region is reproduced when an appropriate prior is supplied, and is provably unconstrained otherwise. This last point is the central methodological observation: the data admits a non-trivial shadow distribution — a function reproducing the observable everywhere it is measured but differing arbitrarily where it is not. Quantifying the uncertainty in the unmeasured region therefore requires sampling this null space, not propagating data noise alone. We discuss the inverse-problem framing, the prior-dependence of the extrapolation, and next steps: MC-dropout and stochastic weight averaging for null-space sampling, sensitivity analysis via the input–output Jacobian to identify the most informative kinematic regions, which provides opportunities to understand the impact of data provided by future experiments and facilities.