A fitter code for Deep Virtual Compton Scattering and Generalized Parton Distributions

TL;DR

This paper introduces a new fitting code for Deep Virtual Compton Scattering that effectively extracts Generalized Parton Distributions from experimental data, enabling detailed insights into nucleon structure.

Contribution

The paper presents a novel fitting code based on the leading-twist handbag DVCS amplitude, capable of extracting GPD information from data with high accuracy and practical application to recent experimental results.

Findings

Full GPD information can be recovered with sufficient observables.

Partial observables still provide valuable GPD constraints.

Application to Jefferson Lab data yields numerical GPD constraints.

Abstract

We have developped a fitting code based on the leading-twist handbag Deep Virtual Compton Scattering (DVCS) amplitude in order to extract the Generalized Parton Distributions (GPD) information from DVCS observables in the valence region. In a first stage, with simulations and pseudo-data, we show that the full GPD information can be recovered from experimental data if enough observables are measured. If only part of these observables are measured, valuable information can still be extracted, certain observables being particularly sensitive to certain GPDs. In a second stage, we make a practical application of this code to the recent DVCS Jefferson Lab Hall A data from which we can extract numerical constraints for the two $H$ GPD Compton Form Factors.