Speaker
Description
Understanding and controlling systematic uncertainties is one of the main challenges in lattice QCD calculations. In this work, we investigate the momentum transfer ($t$) dependence of Mellin moments for unpolarized proton generalized parton distributions (GPDs) at zero skewness. The ground-state matrix elements are extracted using plateau fits and subsequently renormalized using the double ratio method. In this framework, we employ Artificial Neural Network (ANN) to establish the $t$-dependence of the Mellin moments, where the training is performed with the Ioffe-time distribution of GPDs via short-distance factorization. Using this framework, we investigate discretization effects in the matrix elements and the moments. The effect of excited states is investigated using two-state fits. This method enables us to account for the contribution from the first excited state in the underlying matrix elements.
