Statistical aspects of nuclear mass models
| Autor: | Kejzlar, Vojtech, Neufcourt, Léo, Nazarewicz, Witold, Reinhard, Paul-Gerhard |
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| Rok vydání: | 2020 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1088/1361-6471/ab907c |
| Popis: | 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. Comment: 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)," |
| Databáze: | arXiv |
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