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A Unified Neural-Network Framework for Nucleon Imaging from Numerical Simulations of QCD

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arxiv 2509.15931 v2 pith:G7SH4WU7 submitted 2025-09-19 hep-lat hep-exhep-ph

A Unified Neural-Network Framework for Nucleon Imaging from Numerical Simulations of QCD

classification hep-lat hep-exhep-ph
keywords distributionsapproachdataneural-networkpartonelementsframeworkfuture
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Parton distributions encode the momentum-space structure and, in their generalizations, the spatial tomography of quarks and gluons inside hadrons, the building blocks of visible matter. We present a unified neural-network approach that learns these distributions directly from matrix elements calculated via numerical simulations of quantum chromodynamics (QCD) on the lattice by fitting two complementary inputs simultaneously: data matched to physical quantities via known momentum-space and coordinate-space formalisms. Utilizing data from both methods stabilizes the extraction and mitigates biases that can arise when either is used alone. We validate the method on controlled mock data and apply it to lattice-QCD matrix elements to extract parton distribution functions (PDFs). We show benefits of such an approach for determining the physical quantities. We further extend the framework to zero-skewness generalized parton distributions and demonstrate nucleon tomography within the same neural-network parameterization. Our results provide an adaptable and systematically improvable approach for extracting partonic distributions from Euclidean correlators. It can incorporate polarization, additional channels, and future experimental constraints from current and future facilities, such as the Electron-Ion Collider.

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Forward citations

Cited by 7 Pith papers

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