Multi-task deep neural network for predicting both nuclear fission yields and their experimental errors in peak-shaped data

The fission product yield (FPY) is crucially important information for numerous nuclear applications. However, the peak-shaped characteristics of FPY data present important challenges for predicting unobservable FPY data. To address these challenges, after applying multi-task learning models to fission product yield data, and their experimental error estimates, we introduce a novel loss function along with the incorporation of the odd–even effect. Our approach is intended to predict unknown fission yields and the associated experimental errors. To demonstrate the effectiveness of our proposed method, we compared our proposed method with conventional methods that learn each dataset independently. Our findings demonstrate that the proposed methods can predict peak-shaped data with experimental error estimates more effectively than earlier methods can.