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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2601.01495 |
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| _version_ | 1866909981120397312 |
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| author | He, Jian-Yao Chen, Xun Zhu, Xiao-Yan Luo, Wen |
| author_facet | He, Jian-Yao Chen, Xun Zhu, Xiao-Yan Luo, Wen |
| contents | Uncovering physical laws from experimental data is a fundamental goal of theoretical physics. In this work, we apply the spline-based, interpretable Kolmogorov-Arnold Network (KAN) to explore the algebraic structure underlying the baryon octet and decuplet mass spectra. Within a symbolic regression framework and without imposing theoretical priors, KAN autonomously recovers the classical Gell-Mann-Okubo mass relations and accurately extracts the associated SU(3) symmetry-breaking parameters. Compared to conventional fitting approaches, this method achieves comparable predictive accuracy while offering substantially improved interpretability and analytic transparency. Our results demonstrate the potential of KAN as a powerful tool for symbolic discovery in hadron physics and for bridging data-driven modeling with fundamental physical laws. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01495 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Discovering the Gell-Mann-Okubo Formula with Kolmogorov-Arnold Networks He, Jian-Yao Chen, Xun Zhu, Xiao-Yan Luo, Wen High Energy Physics - Phenomenology Uncovering physical laws from experimental data is a fundamental goal of theoretical physics. In this work, we apply the spline-based, interpretable Kolmogorov-Arnold Network (KAN) to explore the algebraic structure underlying the baryon octet and decuplet mass spectra. Within a symbolic regression framework and without imposing theoretical priors, KAN autonomously recovers the classical Gell-Mann-Okubo mass relations and accurately extracts the associated SU(3) symmetry-breaking parameters. Compared to conventional fitting approaches, this method achieves comparable predictive accuracy while offering substantially improved interpretability and analytic transparency. Our results demonstrate the potential of KAN as a powerful tool for symbolic discovery in hadron physics and for bridging data-driven modeling with fundamental physical laws. |
| title | Discovering the Gell-Mann-Okubo Formula with Kolmogorov-Arnold Networks |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2601.01495 |