Application of kernel ridge regression in predicting neutron-capture reaction cross-sections

This article provides the first application of the machine-learning approach in the study of the cross-sections for neutron-capture reactions with the kernel ridge regression (KRR) approach. It is found that the KRR approach can reduce the root-mean-square (rms) deviation of the relative errors betw...

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Bibliographic Details
Published in:Communications in theoretical physics 2022-09, Vol.74 (9), p.95302
Main Authors: Huang, T X, Wu, X H, Zhao, P W
Format: Article
Language:English
Subjects:
kernel ridge regression
machine learning
neutron-capture reaction
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Summary:This article provides the first application of the machine-learning approach in the study of the cross-sections for neutron-capture reactions with the kernel ridge regression (KRR) approach. It is found that the KRR approach can reduce the root-mean-square (rms) deviation of the relative errors between the experimental data of the Maxwellian-averaged ( n , γ ) cross-sections and the corresponding theoretical predictions from 69.8% to 35.4%. By including the data with different temperatures in the training set, the rms deviation can be further significantly reduced to 2.0%. Moreover, the extrapolation performance of the KRR approach along different temperatures is found to be effective and reliable.
ISSN:0253-6102
1572-9494
DOI:10.1088/1572-9494/ac763b