Speaker
Huey-Wen Lin
(Michigan State University)
Description
Generalized parton distributions (GPDs) provide a unified description of the three-dimensional structure of hadrons, encoding both spatial and momentum information. Their determination, however, remains a challenging inverse problem due to limited experimental constraints and theoretical complexities in their extraction. Recent advances in lattice QCD (LQCD) have made it possible to access the Bjorken-$x$ dependence of hadron structure, moving beyond the traditional restriction to a few low moments.
In this talk, we present the extraction of GPDs via machine-learning frameworks from LQCD calculations performed at physical pion mass.
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Author
Huey-Wen Lin
(Michigan State University)