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