# Unbiased determination of DVCS Compton Form Factors

## TL;DR

This paper presents a comprehensive, model-independent extraction of DVCS Compton Form Factors using neural networks and advanced statistical methods, providing new insights into proton structure from experimental data.

## Contribution

It introduces a neural network-based approach for unbiased CFF extraction, reducing model dependence and incorporating uncertainty propagation in DVCS analysis.

## Key findings

- Neural network parameterizations of CFFs with reduced model bias
- Successful propagation of experimental uncertainties to CFFs
- Determination of the subtraction constant via dispersion relations

## Abstract

The extraction of Compton Form Factors (CFFs) in a global analysis of almost all Deeply Virtual Compton Scattering (DVCS) proton data is presented. The extracted quantities are DVCS sub-amplitudes and the most basic observables which are unambiguously accessible from this process. The parameterizations of CFFs are constructed utilizing the artificial neural network technique allowing for an important reduction of model dependency. The analysis consists of such elements as feasibility studies, training of neural networks with the genetic algorithm and a careful regularization to avoid over-fitting. The propagation of experimental uncertainties to extracted quantities is done with the replica method. The resulting parameterizations of CFFs are used to determine the subtraction constant through dispersion relations. The analysis is done within the PARTONS framework.