| THE PHYSICS OF ELEMENTARY PARTICLES AND FIELDS |
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Approach the Gell-Mann–Okubo Formula with Machine Learning |
| Zhenyu Zhang1,2, Rui Ma1,2, Jifeng Hu1,2*, and Qian Wang1,2* |
1Guangdong Provincial Key Laboratory of Nuclear Science, Institute of Quantum Matter, South China Normal University, Guangzhou 510006, China
2Guangdong-Hong Kong Joint Laboratory of Quantum Matter, Southern Nuclear Science Computing Center, South China Normal University, Guangzhou 510006, China
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| Cite this article: |
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Zhenyu Zhang, Rui Ma, Jifeng Hu et al 2022 Chin. Phys. Lett. 39 111201 |
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Abstract Machine learning is a novel and powerful technology and has been widely used in various science topics. We demonstrate a machine-learning-based approach built by a set of general metrics and rules inspired by physics. Taking advantages of physical constraints, such as dimension identity, symmetry and generalization, we succeed to approach the Gell-Mann–Okubo formula using a technique of symbolic regression. This approach can effectively find explicit solutions among user-defined observables, and can be extensively applied to studying exotic hadron spectrum.
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Received: 28 August 2022
Published: 14 October 2022
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| PACS: |
12.40.Yx
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(Hadron mass models and calculations)
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12.39.-x
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(Phenomenological quark models)
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11.30.-j
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(Symmetry and conservation laws)
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| [1] | Gell-Mann M 1961 The Eightfold Way: A Theory of Strong Interaction Symmetry (OSTI.GOV Technical Report by U.S. Department of Energy Office of Scientific and Technical Information) |
| [2] | Okubo S 1962 Prog. Theor. Phys. 27 949 |
| [3] | Gell-Mann M 1962 Phys. Rev. 125 1067 |
| [4] | Chen H X, Chen W, Liu X, and Zhu S L 2016 Phys. Rep. 639 1 |
| [5] | Liu Y R, Chen H X, Chen W, Liu X, and Zhu S L 2019 Prog. Part. Nucl. Phys. 107 237 |
| [6] | Chen H X, Chen W, Liu X, Liu Y R, and Zhu S L 2017 Rep. Prog. Phys. 80 076201 |
| [7] | Dong Y, Faessler A, and Lyubovitskij V E 2017 Prog. Part. Nucl. Phys. 94 282 |
| [8] | Lebed R F, Mitchell R E, and Swanson E S 2017 Prog. Part. Nucl. Phys. 93 143 |
| [9] | Guo F K, Hanhart C, Meißner U G, Wang Q, Zhao Q, and Zou B S 2018 Rev. Mod. Phys. 90 015004 |
| [10] | Albuquerque R M, Dias J M, Khemchandani K P, Martínez T A, Navarra F S, Nielsen M, and Zanetti C M 2019 J. Phys. G 46 093002 |
| [11] | Yamaguchi Y, Hosaka A, Takeuchi S, and Takizawa M 2020 J. Phys. G 47 053001 |
| [12] | Guo F K, Liu X H, and Sakai S 2020 Prog. Part. Nucl. Phys. 112 103757 |
| [13] | Brambilla N, Eidelman S, Hanhart C, Nefediev A, Shen C P, Thomas C E, Vairo A, and Yuan C Z 2020 Phys. Rep. 873 1 |
| [14] | Wetzel S J, Melko R G, Scott J, Panju M, and Ganesh V 2020 Phys. Rev. Res. 2 033499 |
| [15] | Hezaveh Y, Levasseur L, and Marshall P 2017 Nature 548 555 |
| [16] | Schmidt M and Lipson H 2009 Science 324 81 |
| [17] | Liu Z and Tegmark M 2021 Phys. Rev. Lett. 126 180604 |
| [18] | Pang L G, Zhou K, Su N, Petersen H, Stöcker H, and Wang X N 2018 Nat. Commun. 9 210 |
| [19] | Lu P Y, Kim S, and Soljačić M 2020 Phys. Rev. X 10 031056 |
| [20] | Udrescu S M and Tegmark M 2020 Sci. Adv. 6 eaay2631 |
| [21] | Chen S H 2002 Evolutionary Computation in Economics and Finance (Berlin: Springer-Verlag) |
| [22] | Koza J R 1992 Genetic Programming: On the Programming of Computers by Means of Natural Selection (Cambridge: MIT Press) |
| [23] | Iten R, Metger T, Wilming H, del R L, and Renner R 2020 Phys. Rev. Lett. 124 010508 |
| [24] | Heisenberg W 1932 Z. Phys. 77 1 |
| [25] | Gell-Mann M 1956 Nuovo Cimento 4 848 |
| [26] | Workman R L et al. (Particle Data Group) 2022 Prog. Theor. Exp. Phys. 2022 083C01 |
| [27] | de Swart J J 1963 Rev. Mod. Phys. 35 916 |
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