The deconvolution problem of deeply virtual Compton scattering

TL;DR

This paper demonstrates that reconstructing generalized parton distributions from deeply virtual Compton scattering data is inherently ambiguous, highlighting the need for multi-channel analysis to accurately extract these distributions.

Contribution

It provides a next-to-leading order analysis showing the non-uniqueness of GPD extraction from DVCS observables, emphasizing the importance of multi-channel approaches.

Findings

GPDs can have negligible effects on DVCS observables.

Deconvolution of GPDs from data is not uniquely solvable.

Multi-channel analysis is necessary for accurate GPD extraction.

Abstract

Generalised parton distributions are instrumental to study both the three-dimensional structure and the energy-momentum tensor of the nucleon, and motivate numerous experimental programmes involving hard exclusive measurements. Based on a next-to-leading order analysis and a careful study of evolution effects, we exhibit non-trivial generalised parton distributions with arbitrarily small imprints on deeply virtual Compton scattering observables. This means that in practice the reconstruction of generalised parton distributions from measurements, known as the deconvolution problem, does not possess a unique solution for this channel. In this Letter we discuss the consequences on the extraction of generalised parton distributions from data and advocate for a multi-channel analysis.