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| 1 | Effect of source size and emission time on the p–p momentum correlation function in the two-proton emission process显示文摘The effect of source size and emission time on the proton-proton(p-p)momentum correlation function(Cpp(q))has been studied systematically.Assuming a spherical Gaussian source with space and time profile according to the function S(r,t)~exp(-r2/2 r02-t/τ)in the correlation function calculation code(CRAB),the results indicate that one Cpp(q)distribution corresponds to a unique combination of source size r0 and emission timeτ.Considering the possible nuclear deformation from a spherical nucleus,an ellipsoidal Gaussian source characterized by the deformation parameter∈=ΔR/R has been simulated.There is almost no difference of Cpp(q)between the results of spherically and ellipsoidally shaped sources with small deformation.These results indicate that a unique source size r0 and emission time could be extracted from the p-p momentum correlation function,which is especially important for identifying the mechanism of twoproton emission from proton-rich nuclei.Furthermore,considering the possible existence of cluster structures within a nucleus,the double Gaussian source is assumed.The results show that the p-p momentum correlation function for a source with or without cluster structures has large systematical differences with the variance of r0 andτ.This may provide a possible method for experimentally observing the cluster structures in proton-rich nuclei. | Long Zhou De-Qing Fang | 2020 | Nuclear Science and Techniques2020，31，5： | 2 |
| 2 | Research on inversion method for complex source-term distributions based on deep neural networks显示文摘This study proposes a source distribution inversion convolutional neural network (SDICNN), which is deep neural network model for the inversion of complex source distributions, to solve inversion problems involving fixed-source distributions. A function is developed to obtain the distribution information of complex source terms from radiation parameters at individual sampling points in space. The SDICNN comprises two components:a fully connected network and a convolutional neural network. The fully connected network mainly extracts the parameter measurement information from the sampling points,whereas the convolutional neural network mainly completes the fine inversion of the source-term distribution. Finally, the SDICNN obtains a high-resolution source-term distribution image. In this study, the proposed source-term inversion method is evaluated based on typical geometric scenarios. The results show that, unlike the conventional fully connected neural network, the SDICNN model can extract the two-dimensional distribution features of the source terms, and its inversion results are better. In addition, the effects of the shielding mechanism and number of sampling points on the inversion process are examined. In summary, the result of this study can facilitate the accurate assessment of dose distributions in nuclear facilities. | Yi‑Sheng Hao Zhen Wu Yan‑Heng Pu Rui Qiu Hui Zhang Jun‑Li Li | 2023 | Nuclear Science and Techniques2023，34，12： | 0 |
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