Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach

Frontiers in Physics

Physics

Arvind T. Mohan¹  Bradley S. Meyer²  Matthew Mumpower³  Trevor M. Sprouse³  Amy E. Lovell³  Mengke Li⁴ 

[1] Computational Division, Los Alamos National Laboratory, Los Alamos, NM, United States;Department of Physics and Astronomy, Clemson University, Clemson, SC, United States;Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, United States;Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, United States;Department of Physics and Astronomy, Clemson University, Clemson, SC, United States;

关键词: atomic nuclei;    Bayesian averaging;    binding energies and masses;    Machine Learning-ML;    nuclear physics;    computational physics;   

DOI  :  10.3389/fphy.2023.1198572

 received in 2023-04-01, accepted in 2023-07-07,  发布年份 2023

来源: Frontiers

【 摘 要 】

We present global predictions of the ground state mass of atomic nuclei based on a novel Machine Learning algorithm. We combine precision nuclear experimental measurements together with theoretical predictions of unmeasured nuclei. This hybrid data set is used to train a probabilistic neural network. In addition to training on this data, a physics-based loss function is employed to help refine the solutions. The resultant Bayesian averaged predictions have excellent performance compared to the testing set and come with well-quantified uncertainties which are critical for contemporary scientific applications. We assess extrapolations of the model’s predictions and estimate the growth of uncertainties in the region far from measurements.

【 授权许可】

Unknown   
Copyright © 2023 Mumpower, Li, Sprouse, Meyer, Lovell and Mohan.