Data-driven study of timelike Compton scattering

TL;DR

This paper uses a data-driven approach with neural networks to predict timelike Compton scattering observables based on DVCS data, testing the universality of generalized parton distributions and leading-twist dominance.

Contribution

It introduces a neural network-based method to derive TCS amplitudes from DVCS data, reducing model dependency and enabling rigorous tests of theoretical assumptions.

Findings

Successful prediction of TCS observables from DVCS data

Validation of leading-twist dominance in TCS and DVCS amplitudes

Enhanced understanding of GPD universality and nucleon tomography

Abstract

In the framework of collinear QCD factorization, the leading twist scattering amplitudes for deeply virtual Compton scattering (DVCS) and timelike Compton scattering (TCS) are intimately related thanks to analytic properties of leading and next-to-leading order amplitudes. We exploit this welcome feature to make data-driven predictions for TCS observables to be measured in near future experiments. Using a recent extraction of DVCS Compton form factors from most of the existing experimental data for that process, we derive TCS amplitudes and calculate TCS observables only assuming leading-twist dominance. Artificial neural network techniques are used for an essential reduction of model dependency, while a careful propagation of experimental uncertainties is achieved with replica methods. Our analysis allows for stringent tests of the leading twist dominance of DVCS and TCS amplitudes. Moreover, this study helps to understand quantitatively the complementarity of DVCS and TCS measurements to test the universality of generalized parton distributions, which is crucial e.g. to perform the nucleon tomography.