【摘要】 基于全连接前馈神经网络(FNN)训练了一组模型对超重核α衰变的半衰期进行描述.模型的输入数据包括质量数A、中子数N、质子数Z以及α衰变的Q值等,通过对比理论值和实验值发现,本模型在重核区与实验值符合得很好,而且在整个数据空间,本模型预测的精确度相比广义液滴模型理论计算提高10%–20%,获得了更好的对α衰变半衰期的描述.更多还原

【Abstract】 We trained a set of models based on the fully connected feed-forward neural network(FNN) to predict the α-decay halflives of the superheavy nuclei(SHN). The model was trained with input data set of Q values of α-decay, neutron and proton numbers(N, Z), etc. By comparing the predicted half-lives and the experimental values, the accuracy of the trained neural network can be enhanced by about 10%–20% than the general liquid drop model(GLDM2), and in the superheavy regions, the prediction of neural network model is in good agreement with the experimental values.更多还原