Machine learning analysis of fission product yields

Abstract

Analyzing fission product yields (FPY) is challenging because traditional models, while effective in certain conditions, have limitations in predictive accuracy and handling evolving fission modes. To overcome the limitations, especially in scenarios of limited data availability, machine learning models like gaussian process regression (GPR) and gaussian mixture model (GMM) are used for single-fission yield prediction and uncertainty quantification. The application of machine learning techniques demonstrates their practical utility in areas with constrained data, offering a novel approach for future computational advancements in nuclear physics. Our research aims to identify the most effective method for capturing the distribution of the dataset and extracting high-quality samples. These samples could serve as valuable inputs for more complex probabilistic neural networks like Mixture Density Networks (MDNs).