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arXiv · 2606.30519

Nuclear Mass Models with Deformation Optimized and Diagnosed by Bayesian Analysis with Markov Chain Monte Carlo

Xiangnan Lee·Yi Hua Lam·Zi-Ao Zhang·Jayke Ren
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Abstract

We employ a full Bayesian analysis with adaptive Metropolis-Hastings Markov chain Monte Carlo (BA-MCMC) sampling to systematically study the posterior probability distributions of the strengths of energy terms in optimized nuclear mass models of Bethe-Weizs\"{a}cker variants. Strong correlations of some energy terms for some mass models are revealed through the parameter degeneracy diagnosis. We analyze selected refined models to determine parameter degeneracies while proposing a new macroscopic-microscopic mass model, BWL, which considers quadrupole and high-multipole deformation and shell corrections. All mass models in this work are analyzed and optimized through the BA-MCMC method. Compared with 2242 precise experimental binding energies of AME2020, BWL produces a root-mean-square deviation of 759 keV, particularly improving the description of masses in the light-nuclei and actinide regions. BA-MCMC offers robust inference on parameter degeneracy while providing an optimization method for future nuclear mass models.

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Xiangnan Lee, Yi Hua Lam, Zi-Ao Zhang, Jayke Ren. 2026-06-29. Nuclear Mass Models with Deformation Optimized and Diagnosed by Bayesian Analysis with Markov Chain Monte Carlo. https://doi.org/10.1088/1674-1137%2Faea548

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