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Nuclear masses learned from a probabilistic neural network

A. E. Lovell, A. T. Mohan, T. M. Sprouse, and M. R. Mumpower
Phys. Rev. C 106, 014305 – Published 13 July 2022

Abstract

Machine learning methods and uncertainty quantification have been gaining interest throughout the last several years in low-energy nuclear physics. In particular, Gaussian processes and Bayesian neural networks have increasingly been applied to improve mass model predictions while providing well-quantified uncertainties. In this work, we use the probabilistic Mixture Density Network (MDN) to directly predict the mass excess of the 2016 Atomic Mass Evaluation within the range of measured data, and we extrapolate the inferred models beyond available experimental data. The MDN provides not only mean values but also full posterior distributions both within the training set and extrapolated testing set. We show that the addition of physical information to the feature space increases the accuracy of the match to the training data as well as provides for more physically meaningful extrapolations beyond the the limits of experimental data.

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  • Received 15 September 2021
  • Accepted 8 June 2022

DOI:https://doi.org/10.1103/PhysRevC.106.014305

©2022 American Physical Society

Physics Subject Headings (PhySH)

Nuclear Physics

Authors & Affiliations

A. E. Lovell1,*, A. T. Mohan2, T. M. Sprouse1, and M. R. Mumpower1

  • 1Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
  • 2Computational Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

  • *lovell@lanl.gov

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Vol. 106, Iss. 1 — July 2022

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