Statistical aspects of nuclear mass models. (28th July 2020)

• Record Type: Journal Article
• Authors: Kejzlar, V; Neufcourt, L; Nazarewicz, W; Reinhard, P-G
• Is Part Of: Journal of physics. Volume 47:Number 9(2020:Sep.)
• Publication Date: 2020-07-28
• Subjects: nuclear masses -- density functional theory -- Bayesian machine learning -- liquid drop model -- Bayesian model averaging -- model calibration -- principal component analysis; Nuclear physics -- Periodicals; Particles (Nuclear physics) -- Periodicals; Physique nucléaire -- Périodiques; Particules (Physique nucléaire) -- Périodiques; Kernfysica; Elementaire deeltjes; 539.7
• DOI: 10.1088/1361-6471/ab907c
• Languages: English
• ISSNs: 0954-3899

Abstract

Abstract: 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 four-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 quite a 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.