[Submitted on 11 Feb 2020]
Statistical aspects of nuclear mass models
Vojtech Kejzlar · Léo Neufcourt · Witold Nazarewicz · Paul-Gerhard Reinhard
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.
- Comments:
- Accepted for publication in J. Phys. G Focus Issue on "Focus on further enhancing the interaction between nuclear experiment and theory through information and statistics (ISNET 2.0),"
- Subjects:
- Nuclear Theory (nucl-th); stat.AP (stat.AP); Machine Learning (stat.ML)
- arXiv:
- 2002.04151 [pdf]