Uncertainty aware machine-learning-based surrogate models for particle accelerators: Study at the Fermilab Booster Accelerator Complex
Malachi Schram, Kishansingh Rajput, Karthik Somayaji NS, Peng Li, Jason St. John, Himanshu Sharma
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.