NUCLEAR TECHNIQUES, Volume. 46, Issue 8, 080013(2023)
Studies of nuclear β-decay half-lives with Bayesian neural network approach
Fig. 1. RMS deviations from experimental data σRMS(lgT1/2) calculated by BNN-I3, BNN-I4, and BNN-I5 approaches for training set (a) and validation set (b)
Fig. 2. Nuclear β-decay half-lives and errors for Sn isotopes predicted by BNN-I2, BNN-I3, and BNN-I4 approaches[5]
Fig. 3. Comparison of nuclear β-decay half-lives of Ni, Sn, and Pb isotopic chains predicted by BNN-I4 with the theoretical results of RHB+QRPA, FRDM+QRPA, SHFB+FAM, SHFB+QRPA, and WS4+GT
Fig. 4. Comparison of nuclear β-decay half-lives of N=50, N=82, and N=126 isotonic chains predicted by BNN-I4 with the theoretical results of RHB+QRPA, FRDM+QRPA, SHFB+FAM, SHFB+QRPA, and WS4+GT
Fig. 5. Logarithmic difference distribution on the nuclear chart between the predicted β-decay half-lives by BNN-I4 and experimental data
Fig. 6. Logarithmic difference of β-decay half-lives between the BNN-I4 predictions and theoretical results of WS4+GT
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Weifeng LI, Xiaoyan ZHANG, Zhongming NIU. Studies of nuclear β-decay half-lives with Bayesian neural network approach[J]. NUCLEAR TECHNIQUES, 2023, 46(8): 080013
Category: Research Articles
Received: Feb. 28, 2023
Accepted: --
Published Online: Sep. 19, 2023
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