Uncertainty aware machine-learning-based surrogate models for particle accelerators: Study at the Fermilab Booster Accelerator Complex

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Abstract
Standard deep learning methods, such as Ensemble Models, Bayesian Neural Networks, and Quantile Regression Models provide estimates of prediction uncertainties for data-driven deep learning models. However, they can be limited in their applications due to their heavy memory, inference cost, and ability to properly capture out-of-distribution uncertainties. Additionally, some of these models require post-training calibration that limits their ability to be used for continuous learning applications. In this paper, we present a new approach to provide prediction with calibrated uncertainties that includes out-of-distribution contributions and compare it to standard methods on the Fermi National Accelerator Laboratory (FNAL) Booster accelerator complex.
Funding Information
  • U.S. Department of Energy (DE-AC05-76RL01830, DE-AC05-00OR22725, DE-AC05-06OR23177)

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