Uncertainty aware deep learning for particle accelerators

Deep learning models for particle accelerator control and optimization can suffer from overconfidence when deployed to new operating conditions. This paper presents techniques for quantifying uncertainty in deep learning predictions, enabling safer and more reliable deployment in accelerator environments.

arXiv Preprint, arXiv:2309.14502