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Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness.
- Source :
-
European Physical Journal C -- Particles & Fields . Feb2024, Vol. 84 Issue 2, p1-15. 15p. - Publication Year :
- 2024
-
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 ξ which has only been brushed in the literature so far, paving the way for a joint extraction of GPDs. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14346044
- Volume :
- 84
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- European Physical Journal C -- Particles & Fields
- Publication Type :
- Academic Journal
- Accession number :
- 176095788
- Full Text :
- https://doi.org/10.1140/epjc/s10052-024-12513-2