1. Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness
- Author
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Michael Joseph Riberdy, Hervé Dutrieux, Cédric Mezrag, and Paweł Sznajder
- Subjects
Astrophysics ,QB460-466 ,Nuclear and particle physics. Atomic energy. Radioactivity ,QC770-798 - Abstract
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 $$\xi $$ ξ which has only been brushed in the literature so far, paving the way for a joint extraction of GPDs.
- Published
- 2024
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