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

Journal Article · 26 February 2024 · European Physical Journal. C, Particles and Fields (Online)

DOI: https://doi.org/10.1140/epjc/s10052-024-12513-2 · OSTI ID:2315052

Riberdy, Michael Joseph ; Dutrieux, Hervé ; Mezrag, Cédric ; Sznajder, Paweł

Abstract 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 $$$$\xi $$$$ $\xi$ which has only been brushed in the literature so far, paving the way for a joint extraction of GPDs.

• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES)
• Grant/Contract Number: FG02-04ER41302
• OSTI ID: 2315052
• Journal Information: European Physical Journal. C, Particles and Fields (Online), Journal Name: European Physical Journal. C, Particles and Fields (Online) Journal Issue: 2 Vol. 84; ISSN 1434-6052
• Publisher: Springer Science + Business MediaCopyright Statement
• Country of Publication: Germany
• Language: English