https://doi.org/10.1140/epjc/s10052-024-12513-2
Regular Article - Theoretical Physics
Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness
1
IRFU, CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
2
Department of Physics, The College of William and Mary, 23187, Williamsburg, VA, USA
3
National Centre for Nuclear Research (NCBJ), 02-093, Warsaw, Poland
Received:
6
June
2023
Accepted:
29
January
2024
Published online:
27
February
2024
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). Moke lattice QCD data inputs are included in a Bayesian framework to a prior model based on an Artificial Neural Network. This prior model is fitted to reproduce the most experimentally accessible information of a phenomenological extraction by Goloskokov and Kroll. We highlight the impact of the precision, correlation and kinematic coverage of lattice data on GPD extraction at moderate 
which has only been brushed in the literature so far, paving the way for a joint extraction of GPDs.
© The Author(s) 2024
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Funded by SCOAP3.
