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We present a systematic study demonstrating the impact of lattice QCD data onthe extraction of generalised parton distributions (GPDs). For this purpose, weuse a previously developed modelling of GPDs based on machine learningtechniques fulfilling the theoretical requirements of polynomiality, a form ofpositivity constraint and known reduction limits. A special care is given toestimate the uncertainty stemming from the ill-posed character of theconnection between GPDs and the experimental processes usually considered toconstrain them, like deeply virtual Compton scattering (DVCS). Mock lattice QCDdata inputs are included in a Bayesian framework to the prior model which isfitted to reproduce the most experimentally accessible information of aphenomenological model by Goloskov and Kroll. We highlight the impact of theprecision, correlation and kinematic coverage of lattice data on GPD extractionat moderate $\xi$ which has only been brushed in the literature so far, pavingthe way for a joint extraction of GPDs.

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