Speaker
Description
The feasibility of extracting generalized parton distributions (GPDs) unambiguously from deeply-virtual Compton scattering data (DVCS) has recently been questioned due to the existence of an infinite set of so-called ``shadow GPDs'' (SGPDs). These SGPDs are process-dependent, and manifest as multiple solutions---at a fixed $Q^2$---to the inverse problem in DVCS that needs to be solved to obtain the GPDs. That is, SGPDs characterize different possible solutions to this inverse problem that each give identical contributions to observables for a given $Q^2$. We revisit the extent that scale evolution can provide constraints on SGPDs. This is possible because the known classes of SGPDs begin to contribute to observables after evolution to a different $Q^2$ and can no longer be considered SGPDs. Therefore, these SGPDs can be constrained by data that has a finite $Q^2$ range. We separately conduct this analysis for the $H$ and $E$ GPDs, and discuss the impact that the SGPDs could have on the determination of the spin sum, pressure and sheer force distributions, and tomography. Our key finding is that scale evolution, coupled with data over a wide range of $\xi$ and $Q^2$, can constrain the known classes of SGPDs and make possible the extraction of GPDs from DVCS data over a limited range in the GPD variables.
