Bayesian method for fitting the low-energy constants in chiral perturbation theory

Hao-Xiang Pan, De-Kai Kong, Qiao-Yi Wen, Shao-Zhou Jiang

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Front. Phys. ›› 2024, Vol. 19 ›› Issue (6) : 64203. DOI: 10.1007/s11467-024-1430-7
RESEARCH ARTICLE

Bayesian method for fitting the low-energy constants in chiral perturbation theory

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Abstract

The values of the low-energy constants (LECs) are very important in the chiral perturbation theory. This paper adopts a Bayesian method with the truncation errors to globally fit eight next-to-leading order (NLO) LECs Li r and next-to-next-leading order (NNLO) LECs Cir. With the estimation of the truncation errors, the fitting results of Lir in the NLO and NNLO are very close. The posterior distributions of Cir indicate the boundary-dependent relations of these Cir. Ten Cir are weakly dependent on the boundaries and their values are reliable. The other Cir are required more experimental data to constrain their boundaries. Some linear combinations of Ci r are also fitted with more reliable posterior distributions. If one knows some more precise values of Cir, some other Cir can be obtained by these values. With these fitting LECs, most observables provide a good convergence, except for the π K scattering lengths a03 /2 and a0 1/2. An example is also introduced to test the improvement of the method. All the computations indicate that considering the truncation errors can improve the global fit greatly, and more prior information can obtain better fitting results. This fitting method can be extended to the other effective field theories and the perturbation theory.

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Keywords

chiral perturbation theory / low-energy constants / Bayesian statistics

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Hao-Xiang Pan, De-Kai Kong, Qiao-Yi Wen, Shao-Zhou Jiang. Bayesian method for fitting the low-energy constants in chiral perturbation theory. Front. Phys., 2024, 19(6): 64203 https://doi.org/10.1007/s11467-024-1430-7

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Declarations

The authors declare that they have no competing interests and there are no conflicts.

Acknowledgements

H. X. Pan thanks Qin-He Yang for providing the original program. This project was supported by the Guangxi Science Foundation under Grants No. 2022GXNSFAA035489.

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