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Effect of Nuclear Binding Energy to K Factor
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作者 HOU Zhao-Yu GUO Ai-Qiang 《Communications in Theoretical Physics》 SCIE CAS CSCD 2007年第4期690-694,共5页
We modify the square of virtual photon four-momentum by using nuclear binding energy formula, and calculate the effect of nuclear binding energy to K factor and Compton subprocess and annihilate subprocess in A-A coll... We modify the square of virtual photon four-momentum by using nuclear binding energy formula, and calculate the effect of nuclear binding energy to K factor and Compton subprocess and annihilate subprocess in A-A collision Drell-Yan process. The outcome indicates that the effect of nuclear binding energy to K factor is obvious in little x region and it would disappear gradually as x increases. 展开更多
关键词 Drell-Yan process nuclear binding energy K factor
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Investigation of Nuclear Binding Energy and Charge Radius Based on Random Forest Algorithm
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作者 CAI Boshuai YU Tianjun +3 位作者 LIN Xuan ZHANG Jilong WANG Zhixuan YUAN Cenxi 《原子能科学技术》 EI CAS CSCD 北大核心 2023年第4期704-712,共9页
The random forest algorithm was applied to study the nuclear binding energy and charge radius.The regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random forest.RMSE ... The random forest algorithm was applied to study the nuclear binding energy and charge radius.The regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random forest.RMSE for nuclides with Z,N>7 is reduced to 0.816 MeV and 0.0200 fm compared with the six-term liquid drop model and a three-term nuclear charge radius formula,respectively.Specific interest is in the possible(sub)shells among the superheavy region,which is important for searching for new elements and the island of stability.The significance of shell features estimated by the so-called shapely additive explanation method suggests(Z,N)=(92,142)and(98,156)as possible subshells indicated by the binding energy.Because the present observed data is far from the N=184 shell,which is suggested by mean-field investigations,its shell effect is not predicted based on present training.The significance analysis of the nuclear charge radius suggests Z=92 and N=136 as possible subshells.The effect is verified by the shell-corrected nuclear charge radius model. 展开更多
关键词 nuclear binding energy nuclear charge radius random forest algorithm
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Isospin dependence of the nuclear binding energy
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作者 Y.Q.He J.K.Ge +1 位作者 G.J.Fu H.Jiang 《Chinese Physics C》 SCIE CAS CSCD 2021年第1期272-279,共8页
In this paper,we study the symmetry energy and the Wigner energy in the binding energy formula for atomic nuclei.We simultaneously extract the I2 symmetry energy and Wigner energy coefficients using the double differe... In this paper,we study the symmetry energy and the Wigner energy in the binding energy formula for atomic nuclei.We simultaneously extract the I2 symmetry energy and Wigner energy coefficients using the double difference of "experimental" symmetry-Wigner energies,based on the binding energy data of nuclei with A≥16.Our study of the triple difference formula and the "experimental" symmetry-Wigner energy suggests that the macroscopic isospin dependence of binding energies is explained well by the I2 symmetry energy and the Wigner energy,and further consideration of the I4 term in the binding energy formula does not substantially improve the calculation result. 展开更多
关键词 symmetry energy i^4 term Wigner energy nuclear binding energy
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Reliable calculations of nuclear binding energies by the Gaussian process of machine learning
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作者 Zi-Yi Yuan Dong Bai +1 位作者 Zhen Wang Zhong-Zhou Ren 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2024年第6期130-144,共15页
Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the ... Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process. First, the binding energies for 2238 nuclei with Z > 20 and N > 20 are calculated using the Gaussian process in a physically motivated feature space, yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV. The results show the good learning ability of the Gaussian process in the studies of binding energies. Then, the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020. The theoretical results are in good agreement with the experimental data, reflecting the good predictive power of the Gaussian process. Moreover, the α-decay energies for 1169 nuclei with 50 ≤ Z ≤ 110 are derived from the theoretical binding energies calculated using the Gaussian process. The average deviation and the standard deviation are, respectively, 0.047 MeV and 0.070 MeV. Noticeably, the calculated α-decay energies for the two new isotopes ^ (204 )Ac(Huang et al. Phys Lett B 834, 137484(2022)) and ^ (207) Th(Yang et al. Phys Rev C 105, L051302(2022)) agree well with the latest experimental data. These results demonstrate that the Gaussian process is reliable for the calculations of nuclear binding energies. Finally, the α-decay properties of some unknown actinide nuclei are predicted using the Gaussian process. The predicted results can be useful guides for future research on binding energies and α-decay properties. 展开更多
关键词 nuclear binding energies DECAY Machine learning Gaussian process
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