Predictions of nuclear β-decay half-lives with machine learning and their impact on r-process nucleosynthesis

Z. M. Niu (牛中明), H. Z. Liang (梁豪兆), B. H. Sun (孙保华), W. H. Long (龙文辉), and Y. F. Niu (牛一斐)
Phys. Rev. C 99, 064307 – Published 5 June 2019

Abstract

Nuclear β decay is a key process to understand the origin of heavy elements in the universe, while the accuracy is far from satisfactory for the predictions of β-decay half-lives by nuclear models to date. In this work, we pave a novel way to accurately predict β-decay half-lives with the machine learning based on the Bayesian neural network, in which the known physics has been explicitly embedded, including the ones described by the Fermi theory of β decay, and the dependence of half-lives on pairing correlations and decay energies. The other potential physics, which is not clear or even missing in nuclear models nowadays, will be learned by the Bayesian neural network. The results well reproduce the experimental data with a very high accuracy and further provide reasonable uncertainty evaluations in half-life predictions. These accurate predictions for half-lives with uncertainties are essential for the r-process simulations.

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  • Received 2 October 2018
  • Revised 7 April 2019

DOI:https://doi.org/10.1103/PhysRevC.99.064307

©2019 American Physical Society

Physics Subject Headings (PhySH)

Nuclear PhysicsInterdisciplinary Physics

Authors & Affiliations

Z. M. Niu (牛中明)1,2, H. Z. Liang (梁豪兆)3,4,*, B. H. Sun (孙保华)5, W. H. Long (龙文辉)6, and Y. F. Niu (牛一斐)6,7

  • 1School of Physics and Materials Science, Anhui University, Hefei 230601, China
  • 2Institute of Physical Science and Information Technology, Anhui University, Hefei 230601, China
  • 3RIKEN Nishina Center, Wako 351-0198, Japan
  • 4Department of Physics, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan
  • 5School of Physics and Nuclear Energy Engineering, Beihang University, Beijing 100191, China
  • 6School of Nuclear Science and Technology, Lanzhou University, Lanzhou 730000, China
  • 7ELI-NP, “Horia Hulubei” National Institute for Physics and Nuclear Engineering, RO-077125, Bucharest-Magurele, Romania

  • *haozhao.liang@riken.jp

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Issue

Vol. 99, Iss. 6 — June 2019

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