A Unified Neural-Network Framework for Nucleon Imaging from Numerical Simulations of QCD
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913173169242112 |
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| author | Chu, Min-Huan Cichy, Krzysztof Constantinou, Martha Sznajder, Paweł Wagner, Jakub |
| author_facet | Chu, Min-Huan Cichy, Krzysztof Constantinou, Martha Sznajder, Paweł Wagner, Jakub |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15931 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Unified Neural-Network Framework for Nucleon Imaging from Numerical Simulations of QCD Chu, Min-Huan Cichy, Krzysztof Constantinou, Martha Sznajder, Paweł Wagner, Jakub High Energy Physics - Lattice High Energy Physics - Experiment High Energy Physics - Phenomenology 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. |
| title | A Unified Neural-Network Framework for Nucleon Imaging from Numerical Simulations of QCD |
| topic | High Energy Physics - Lattice High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2509.15931 |