Reconstruction of Gravitational Form Factors using Generative Machine Learning
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914371465117696 |
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| author | Alharazin, Herzallah Panteleeva, Julia Yu. |
| author_facet | Alharazin, Herzallah Panteleeva, Julia Yu. |
| contents | We develop a generative framework based on denoising diffusion for the model-independent reconstruction of hadronic form factors from sparse and noisy data. The generative prior is built from a large ensemble of synthetic curves drawn from ten distinct functional classes rooted in different theoretical approaches to hadron structure. Applied to the proton gravitational form factors $A(t)$, $J(t)$, and $D(t)$, the framework yields non-parametric reconstructions consistent with lattice QCD across the full kinematic range $0\le -t\le 2~\mathrm{GeV}^{2}$, remaining robust even when only one or two conditioning points are retained. The densely sampled output enables a direct extraction of the chiral low-energy constants $c_8=-4.6\pm 0.8~\mathrm{GeV}^{-1}$ and $c_9=-0.61\pm 0.19~\mathrm{GeV}^{-1}$. Using these values at the physical pion mass, we obtain $D(0)=-4.3\pm 0.8$ for the nucleon $D$-term. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19267 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reconstruction of Gravitational Form Factors using Generative Machine Learning Alharazin, Herzallah Panteleeva, Julia Yu. High Energy Physics - Phenomenology High Energy Physics - Lattice High Energy Physics - Theory We develop a generative framework based on denoising diffusion for the model-independent reconstruction of hadronic form factors from sparse and noisy data. The generative prior is built from a large ensemble of synthetic curves drawn from ten distinct functional classes rooted in different theoretical approaches to hadron structure. Applied to the proton gravitational form factors $A(t)$, $J(t)$, and $D(t)$, the framework yields non-parametric reconstructions consistent with lattice QCD across the full kinematic range $0\le -t\le 2~\mathrm{GeV}^{2}$, remaining robust even when only one or two conditioning points are retained. The densely sampled output enables a direct extraction of the chiral low-energy constants $c_8=-4.6\pm 0.8~\mathrm{GeV}^{-1}$ and $c_9=-0.61\pm 0.19~\mathrm{GeV}^{-1}$. Using these values at the physical pion mass, we obtain $D(0)=-4.3\pm 0.8$ for the nucleon $D$-term. |
| title | Reconstruction of Gravitational Form Factors using Generative Machine Learning |
| topic | High Energy Physics - Phenomenology High Energy Physics - Lattice High Energy Physics - Theory |
| url | https://arxiv.org/abs/2602.19267 |