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

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...

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Bibliographic Details
Main Authors: Matthew Mumpower, Mengke Li, Trevor M. Sprouse, Bradley S. Meyer, Amy E. Lovell, Arvind T. Mohan
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-07-01
Series:Frontiers in Physics
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fphy.2023.1198572/full