Comparison of Neural Network and Hadronic Model Predictions of Two-Photon Exchange Effect

TL;DR

This paper compares two approaches—Bayesian neural network and hadronic box diagrams—for predicting two-photon exchange corrections in elastic electron-proton scattering, analyzing their agreement across different momentum transfer ranges and confronting predictions with experimental data.

Contribution

It provides a detailed comparison of neural network and hadronic model predictions for TPE effects, highlighting their agreement and discrepancies at various Q^2 ranges.

Findings

Good agreement between methods at intermediate Q^2 (1-3 GeV^2)

Discrepancies below Q^2=1 GeV^2

Predictions align with preliminary VEPP-3 experimental results

Abstract

Predictions for the two-photon exchange (TPE) correction to unpolarized $ep$ elastic cross section, obtained within two different approaches, are confronted and discussed in detail. In the first one the TPE correction is extracted from experimental data by applying the Bayesian neural network (BNN) statistical framework. In the other the TPE is given by box diagrams, with the nucleon and the $P_{33}$ resonance as the hadronic intermediate states. Two different form factor parametrizations for both the proton and the $P_{33}$ resonance are taken into consideration. Proton form factors are obtained from the global fit of the full model (with the TPE correction) to the unpolarized cross section data. Predictions of both methods agree well in the intermediate $Q^2$ range, $(1,3)$ GeV$^2$. Above $Q^2=3$ GeV$^2$ the agreement is on $2\sigma$ level. Below $Q^2=1$ GeV$^2$ the consistency between both approaches is broken. The values of the proton radius extracted within both models are given. In both cases predictions for VEPP-3 experiment have been obtained and confronted with the preliminary experimental results.