Uncertainty quantification of mass models using Ensemble Bayesian Model Averaging

Y. Saito, I. Dillmann, R. Krucken, M. Mumpower, R. Surman

Unpublished submitted (2023)

The development in the description of the masses of atomic nuclei has led to various nuclear mass models that can predict the masses across the whole chart of nuclides. These mass models play an important role in understanding the synthesis of heavy elements in the rapid neutron capture process (the r-process). However, it is still a challenging task to estimate the size of uncertainty associated with the predictions of each mass model. In this work, a method to quantify the mass uncertainty using ensemble Bayesian model averaging (EBMA) is introduced. This Bayesian method provides a natural way to perform model averaging, selection, calibration, and uncertainty quantification, by combining the mass models as a mixture of normal distributions, whose parameters are optimized against the experimental data, employing the Markov chain Monte Carlo (MCMC) method using the No-U-Turn sampler (NUTS). The average size of our best uncertainty estimates of neutron separation energies based on the AME2003 data is 0.48 [MeV] and covers 95 % of new data in AME2020. The uncertainty estimates can also be used to detect outliers with respect to the trend of experimental data and theoretical predictions.



machine learning uncertainty quantification

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