Machine learning for neutron source distributions

Abstract

In light of the recent advancements in machine learning (ML), we propose a novel approach to neutron source distribution estimation through the utilization of probabilistic generative models (PGMs). The estimation is based on a Monte Carlo particle list, which is only required during the training stage of the ML model. Once the source distribution has been learned, the model is independent of the original particle list, allowing for further sampling in an efficient, rapid, and memory-costless manner. The performance of various generative models is evaluated, including a variational autoencoder, a normalizing flow, a generative adversarial network, and a denoising diffusion model. These approaches are then compared to existing source distribution estimations, and the advantages and disadvantages of each approach are discussed. The results demonstrate that source distributions can be modeled through the use of PGMs, which paves the way for further advancements in this field.