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

作　　者：T X Huang X H Wu P W Zhao 

机构地区：[1]State Key Laboratory of Nuclear Physics and Technology,School of Physics,Peking University,Beijing 100871,China

出　　处：《Communications in Theoretical Physics》2022年第9期98-104,共7页理论物理通讯（英文版）

基　　金：partly supported by the National Key R&D Program of China(Contracts No.2018YFA0404400 and No.2017YFE0116700);the National Natural Science Foundation of China(Grants No.11875075,No.11935003,No.11975031,No.12141501 and No.12070131001);the China Postdoctoral Science Foundation under Grant No.2021M700256;the High-performance Computing Platform of Peking University

摘　　要：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.

关 键 词：kernel ridge regression machine learning neutron-capture reaction 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] O571[自动化与计算机技术—控制科学与工程]