Assessing the impact of the electron ion collider in China on Deeply Virtual Compton Scattering

TL;DR

This paper evaluates how future measurements at China's Electron-Ion Collider will improve understanding of proton structure through Deeply Virtual Compton Scattering, using neural networks to analyze data and reduce uncertainties.

Contribution

It introduces a neural-network-based framework to parameterize Compton Form Factors and estimates the impact of EicC data on reducing uncertainties in proton structure measurements.

Findings

Significant reduction in CFF uncertainties with EicC data

Strong improvements in the sea-quark region

Enhanced precision in proton structure analysis

Abstract

We assess the impact of future measurements of deeply virtual Compton scattering (DVCS) off protons using the planned detector at the Electron-Ion Collider in China (EicC), proposed as an upgrade to the High Intensity heavy-ion Accelerator Facility (HIAF). We develop a neural-network architecture to flexibly parameterize the Compton Form Factors (CFFs), extrapolate reliably into unmeasured kinematic regions, and provide robust uncertainty estimates through the replica method. The framework is fitted to the available worldwide DVCS data using the Gepard software. We find a significant reduction in the uncertainties of all CFFs after incorporating pseudo-data from single and double polarization asymmetries at the EicC, with particularly strong improvements in the sea-quark region.