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

@misc{lee2026nuclear,
  title = {{Nuclear Mass Models with Deformation Optimized and Diagnosed by Bayesian Analysis with Markov Chain Monte Carlo}},
  author = {Xiangnan Lee and Yi Hua Lam and Zi-Ao Zhang and Jayke Ren},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2606.30519},
  eprint = {2606.30519},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.30519},
}

TY  - UNPB
TI  - Nuclear Mass Models with Deformation Optimized and Diagnosed by Bayesian Analysis with Markov Chain Monte Carlo
AU  - Xiangnan Lee
AU  - Yi Hua Lam
AU  - Zi-Ao Zhang
AU  - Jayke Ren
PY  - 2026
DA  - 2026/09/10
PB  - arXiv
DO  - 10.48550/arxiv.2606.30519
UR  - https://arxiv.org/abs/2606.30519
AB  - 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-Weizsäcker 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.
ER  -