A DNN fit to pion form-factor and lattice data produces quark and gluon GPDs; quark results agree with lattice, gluon results rely on a fitted normalization factor.
Neural Network Generalized Parton Distributions (NNGPD)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Generalized parton distributions (GPDs) serve as indispensable tools for the exploration of proton structure. In this study, we offer a deep learning-assisted framework for the extraction of GPDs from experimental data and the results of ab-initio lattice quantum chromodynamics (LQCD).
citation-role summary
background 1
citation-polarity summary
fields
hep-ph 1years
2026 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Extraction of Pion Unpolarized Quark and Gluon Generalized Parton Distributions using Deep Neural-Networks
A DNN fit to pion form-factor and lattice data produces quark and gluon GPDs; quark results agree with lattice, gluon results rely on a fitted normalization factor.