The Bayesian neural network(BNN)method is proposed to predict the isotopic cross-sections in proton induced spallation reactions.Learning from more than 4000 data sets of isotopic cross-sections from 19 experimental m...The Bayesian neural network(BNN)method is proposed to predict the isotopic cross-sections in proton induced spallation reactions.Learning from more than 4000 data sets of isotopic cross-sections from 19 experimental measurements and 5 theoretical predictions with the SPACS parametrization,in which the mass of the spallation system ranges from 36 to 238,and the incident energy from 200 MeV/u to 1500 MeV/u,it is demonstrated that the BNN method can provide good predictions of the residue fragment cross-sections in spallation reactions.展开更多
Nuclear β-decay half-lives are predicted based on an empirical formula and the mass predictions from various nuclear models.It is found that the empirical formula can reproduce the nuclearβ-decay half-lives well,esp...Nuclear β-decay half-lives are predicted based on an empirical formula and the mass predictions from various nuclear models.It is found that the empirical formula can reproduce the nuclearβ-decay half-lives well,especially for short-lived nuclei with T_(1/2)<1s.The theoretical half-life uncertainties fromβ-decay energies and the parameters of the empirical formula are further investigated.It is found that the uncertainties of the half-lives are relatively large for heavy nuclei and nuclei near the neutron-drip line.For nuclei on the r-process path,the uncertainties for those with N=126 are about one order of magnitude,which are much larger than the uncertainties for those with N=50 and 82.However,theoretical uncertainties from the parameters of the empirical formula are relatively small for the nuclei on the r-process path,which indicates that the empirical formula is very suitable for predicting theβ-decay half-lives in r-process simulations.展开更多
The radial basis function(RBF) approach is a powerful tool to improve nuclear mass predictions. By combining the RBF approach with the latest relativistic continuum Hartree-Bogoliubov(RCHB) model, the local systematic...The radial basis function(RBF) approach is a powerful tool to improve nuclear mass predictions. By combining the RBF approach with the latest relativistic continuum Hartree-Bogoliubov(RCHB) model, the local systematic deviations between the RCHB mass predictions and the experimental data are eliminated, and the root-meansquare(rms) mass deviation is significantly reduced from 7.923 MeV to 0.386 MeV. However, systematic deviations between the RBF improved mass predictions and the experimental data remain for nuclei with four different odd-even parities, i.e.(even Z, even N),(even Z, odd N),(odd Z, even N), and(odd Z, odd N). They can be reduced by separately training RBF for the four groups of nuclei, and the resulting rms deviation decreases to 0.229 MeV. It is found that the RBF approach can describe the deformation effects neglected in the present RCHB mass calculations, and also improves the description of the shell effect and the pairing effect.展开更多
基金Supported by the National Natural Science Foundation of China(11975091,U1732135,11875070)Natural Science Foundation of Henan Province(162300410179)supported by the US Department of Energy(DE-FG02-93ER40773)
文摘The Bayesian neural network(BNN)method is proposed to predict the isotopic cross-sections in proton induced spallation reactions.Learning from more than 4000 data sets of isotopic cross-sections from 19 experimental measurements and 5 theoretical predictions with the SPACS parametrization,in which the mass of the spallation system ranges from 36 to 238,and the incident energy from 200 MeV/u to 1500 MeV/u,it is demonstrated that the BNN method can provide good predictions of the residue fragment cross-sections in spallation reactions.
基金We acknowledge S. Goriely, B. Sun, and P. W. Zhao for stimulating discussions. This work was supported in part by the National Undergraduate Training Programs for Innovation and Entrepreneurship （Project No. 201210635132）, the National Basic Research Program of China （973 Program） （Grant No. 2013CB834400）, the National Natural Science Foundation of China （Grant Nos. 10975008, 10947013, 11175002, 11105110, 11105111, and 11205004）, the Research Fund for the Doctoral Program of Higher Education （Grant No. 20110001110087）, the Natural Science Foundation of Chongqing （Grant No. cstc2011jjA0376）, and the Fundamental Research Funds for the Central Universities （Grant Nos. XDJK2010B007 and XDJK2011B002）.
基金Partly supported by the National Natural Science Foundation of China under(11805004,11875070)the Key Research Foundation of Education Ministry of Anhui Province(KJ2020A0485)the Open fund for Discipline Construction,Institute of Physical Science and Information Technology,Anhui University.
文摘Nuclear β-decay half-lives are predicted based on an empirical formula and the mass predictions from various nuclear models.It is found that the empirical formula can reproduce the nuclearβ-decay half-lives well,especially for short-lived nuclei with T_(1/2)<1s.The theoretical half-life uncertainties fromβ-decay energies and the parameters of the empirical formula are further investigated.It is found that the uncertainties of the half-lives are relatively large for heavy nuclei and nuclei near the neutron-drip line.For nuclei on the r-process path,the uncertainties for those with N=126 are about one order of magnitude,which are much larger than the uncertainties for those with N=50 and 82.However,theoretical uncertainties from the parameters of the empirical formula are relatively small for the nuclei on the r-process path,which indicates that the empirical formula is very suitable for predicting theβ-decay half-lives in r-process simulations.
基金Supported by the National Natural Science Foundation of China(11805004,11875070 and 11711540016)the Natural Science Foundation of Anhui Province(1708085QA10)+2 种基金the Key Research Foundation of Education Ministry of Anhui Province(KJ2016A026 and SK2018A0577)the Doctor Foundation of Anhui Jianzhu University 2017(2017QD18)the Open fund for Discipline Construction,Institute of Physical Science and Information Technology,Anhui University
文摘The radial basis function(RBF) approach is a powerful tool to improve nuclear mass predictions. By combining the RBF approach with the latest relativistic continuum Hartree-Bogoliubov(RCHB) model, the local systematic deviations between the RCHB mass predictions and the experimental data are eliminated, and the root-meansquare(rms) mass deviation is significantly reduced from 7.923 MeV to 0.386 MeV. However, systematic deviations between the RBF improved mass predictions and the experimental data remain for nuclei with four different odd-even parities, i.e.(even Z, even N),(even Z, odd N),(odd Z, even N), and(odd Z, odd N). They can be reduced by separately training RBF for the four groups of nuclei, and the resulting rms deviation decreases to 0.229 MeV. It is found that the RBF approach can describe the deformation effects neglected in the present RCHB mass calculations, and also improves the description of the shell effect and the pairing effect.
