论文标题
借助人工神经网络的α-decay半衰期对中子缺陷核的预测
Predictions of α-decay half-lives for neutron-deficient nuclei with the aid of artificial neural network
论文作者
论文摘要
近年来，人工神经网络（ANN）已成功地应用于核物理和其他一些物理领域。这项研究始于使用库仑和接近电位模型（CPPM），依赖温度的库仑和接近电位模型（CPPMT），Royer经验公式，新的REN B（NRB），新的REN B（NRB）配方和训练有素的艺术神经网络模型（TANN）。与实验值相比，发现ANN模型可以很好地描述中子缺陷核的半衰期。此外，发现CPPMT的性能优于CPPM，这表明使用依赖温度的核电位的重要性。此外，为了预测未衡量的中子缺陷核的α-DECAY半衰期，对另一种ANN算法进行了训练以预测Qα值。将Qα预测的结果与Weizsäcker-Skyrme-4+RBF（WS4+RBF）公式进行了比较。然后使用CPPM，CPPMT，Royer，NRB和Tann预测未衡量的中子缺陷核的半衰期，其Qα值由ANN作为输入预测。这项研究得出结论，可以使用ANN成功预测中子缺陷核的α-decay的半衰期，这可以有助于确定dripline的核。
In recent years, artificial neural network (ANN) has been successfully applied in nuclear physics and some other areas of physics. This study begins with the calculations of α-decay half-lives for some neutron-deficient nuclei using Coulomb and proximity potential model (CPPM), temperature dependent Coulomb and proximity potential model (CPPMT), Royer empirical formula, new Ren B (NRB) formula, and a trained artificial neural network model (TANN ). By comparison with experimental values, the ANN model is found to give very good descriptions of the half-lives of the neutron-deficient nuclei. Moreover CPPMT is found to perform better than CPPM, indicating the importance of employing temperature-dependent nuclear potential. Furthermore, to predict the α-decay half-lives of unmeasured neutron-deficient nuclei, another ANN algorithm is trained to predict the Q α values. The results of the Q α predictions are compared with the Weizsäcker-Skyrme-4+RBF (WS4+RBF) formula. The half-lives of unmeasured neutron-deficient nuclei are then predicted using CPPM, CPPMT, Royer, NRB, and TANN , with Qα values predicted by ANN as inputs. This study concludes that half-lives of α-decay from neutron-deficient nuclei can successfully be predicted using ANN, and this can contribute to the determination of nuclei at the driplines.
