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

TL;DR

This paper introduces a multi-task deep learning approach with a novel loss function to predict nuclear fission yields and their experimental errors, effectively handling peak-shaped data.

Contribution

The study presents a new multi-task learning model with a specialized loss function and odd-even effect incorporation for improved FPY prediction.

Findings

The proposed method outperforms traditional independent learning approaches.

It effectively predicts peak-shaped fission yield data.

The model also estimates experimental errors accurately.

Abstract

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 incorporation of the odd even effect. Our approach is intended to predict unknown fission yields and the associated experimental error. 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.