# Shedding Light on Shadow Generalized Parton Distributions

The potential impact of some examples of Shadow Generalized Parton Distributions (GPDs) on the uncertainties in an extraction of GPDs from data has been explored.

# The Science

When solving the inverse problem to infer GPDs from data, it has been demonstrated that multiple GPD-like functions, or equivalently multiple solutions, can be found that will give the same value for an observable. These multiple solutions are characterized by an infinite number of functions called Shadow GPDs that can actually give zero contribution to an observable. However, examples of Shadow GPDs that have been explored have been demonstrated to no longer give zero contribution to the same observable at a different energy scale. Thus data spanning a range of energy scales could potentially help limit the impact of Shadow GPDs on the uncertainties in phenomenological extractions of GPDs.

# The Impact

The three dimensional imaging of quarks and gluons within hadrons is a key motivation for particle accelerator facilities around the world. Since this information is accessible via the GPDs, properly accounting for Shadow GPDs and understanding how well these functions can be constrained is vital to achieving this goal.

# Summary

An exploration of the impact of data spanning a range of energy scales on constraining a sampling of example Shadow GPDs has demonstrated that at least those functions explored can be well constrained over a limited range of kinematics. Thus demonstrating a necessary condition for future extraction of GPDs from currently available data.

# Contact

Eric Moffat

Argonne National Lab

emoffat@anl.gov

# Publications

- Eric Moffat, Adam Freese, Ian Cloët, Thomas Donohoe, Leonard Gamberg, Wally Melnitchouk, Andreas Metz, Alexei Prokudin, Nobuo Sato,
*Shedding Light on Shadow Generalized Parton Distributions*, Phys.Rev.D 108 (2023) 3, 036027,