# Statistical aspects of nuclear mass models

arXiv: https://arxiv.org/abs/2002.04151 · HTML: https://mlsanity.com/paper/2002.04151/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [Machine Learning in Nuclear Physics](https://mlsanity.com/paper/2112.02309/) (2021)
- [Local Bayesian Dirichlet mixing of imperfect models](https://mlsanity.com/paper/2311.01596/) (2023)
- [Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis](https://mlsanity.com/paper/2405.10839/) (2024)

## Abstract (arXiv)

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.