Shedding light on shadow generalized parton distributions

The feasibility of extracting generalized parton distributions (GPDs) from deeply-virtual Compton scattering (DVCS) data has recently been questioned because of the existence of an infinite set of so-called ''shadow GPDs'' (SGPDs). These SGPDs depend on the process and manifest as multiple solutions (at a fixed scale $Q^2$) to the inverse problem that needs to be solved to infer GPDs from DVCS data. SGPDs therefore pose a significant challenge for extracting GPDs from DVCS data. With this motivation we study the extent to which QCD evolution can provide constraints on SGPDs. This is possible because the known classes of SGPDs begin to contribute to observables after evolution, and can then be constrained (at the input scale $Q^2_0$) by data that has a finite $Q^2$ range. The impact that SGPDs could have on determining the total angular momentum, pressure and sheer force distributions, and tomography is also discussed. Our key finding is that scale evolution, coupled with data over a wide range of skewness $\xi$ and $Q^2$, can constrain the class of SGPDs that we studied and potentially make possible the extraction of GPDs from DVCS data over a limited range in the GPD variables.