Neural Network Generalized Parton Distributions (NNGPD)
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918499452977152 |
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| author | Panjsheeri, Zaki Liuti, Simonetta |
| author_facet | Panjsheeri, Zaki Liuti, Simonetta |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_13000 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neural Network Generalized Parton Distributions (NNGPD) Panjsheeri, Zaki Liuti, Simonetta High Energy Physics - Phenomenology 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). |
| title | Neural Network Generalized Parton Distributions (NNGPD) |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2605.13000 |