The development of compact neutron sources for applications is extensive and features many approaches. Let alone ion-based approaches, several projects with different parameters exist. This article focuses on ion-based neutron production below the spallation barrier for arbitrary light ion beams. With this model, it is possible to compare different ion-based neutron source concepts against each other quickly. This contribution derives a predictive model using Monte Carlo simulations (50k simulations) and deep neural networks. This model can skip the necessary Monte Carlo simulations, which individually take a long time to complete, increasing the effort for optimization and predictions. The models' shortcomings are addressed, and mitigation strategies are proposed.
@article{arxiv.2307.05498,
title = {Neutron Yield Predictions with Artificial Neural Networks: A Predictive Modeling Approach},
author = {Benedikt Schmitz and Stefan Scheuren},
journal= {arXiv preprint arXiv:2307.05498},
year = {2023}
}