Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness

We present a systematic study demonstrating the impact of lattice QCD data on the extraction of generalised parton distributions (GPDs). For this purpose, we use a previously developed modelling of GPDs based on machine learning techniques fulfilling the theoretical requirements of polynomiality, a form of positivity constraint and known reduction limits. A special care is given to estimate the uncertainty stemming from the ill-posed character of the connection between GPDs and the experimental processes usually considered to constrain them, like deeply virtual Compton scattering (DVCS). Mock lattice QCD data inputs are included in a Bayesian framework to the prior model which is fitted to reproduce the most experimentally accessible information of a phenomenological model by Goloskov and Kroll. We highlight the impact of the precision, correlation and kinematic coverage of lattice data on GPD extraction at moderate $\xi$ which has only been brushed in the literature so far, paving the way for a joint extraction of GPDs.