Statistical aspects of nuclear mass models
This work addresses the challenge of refining nuclear mass models for researchers in nuclear physics, but it is incremental as it applies existing statistical methods to this domain.
The study tackled the problem of analyzing the information content of nuclear masses using global models of nuclear binding energies, employing statistical methods like Bayesian calibration and principal component analysis, and demonstrated that significant parameter reduction could be achieved in models such as the Liquid Drop Model and Skyrme energy density functional, with Bayesian model averaging improving uncertainty quantification.
We study the information content of nuclear masses from the perspective of global models of nuclear binding energies. To this end, we employ a number of statistical methods and diagnostic tools, including Bayesian calibration, Bayesian model averaging, chi-square correlation analysis, principal component analysis, and empirical coverage probability. Using a Bayesian framework, we investigate the structure of the 4-parameter Liquid Drop Model by considering discrepant mass domains for calibration. We then use the chi-square correlation framework to analyze the 14-parameter Skyrme energy density functional calibrated using homogeneous and heterogeneous datasets. We show that a quite dramatic parameter reduction can be achieved in both cases. The advantage of Bayesian model averaging for improving uncertainty quantification is demonstrated. The statistical approaches used are pedagogically described; in this context this work can serve as a guide for future applications.