Uncertainty Aware Deep Learning for Particle Accelerators

2023/09/25 by Kishansingh Rajput, Rajput, Kishansingh, Malachi Schram +3 · 1 citation
Engineering · Physics and Astronomy · #Accelerator Physics (physics.acc-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Gamma-ray bursts and supernovae #Machine Learning (cs.LG) #Nuclear reactor physics and engineering #Particle Detector Development and Performance

paper · pdf · doi:10.48550/arxiv.2309.14502

openalex publication_date 2023/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03

Abstract

Standard deep learning models for classification and regression applications are ideal for capturing complex system dynamics. However, their predictions can be arbitrarily inaccurate when the input samples are not similar to the training data. Implementation of distance aware uncertainty estimation can be used to detect these scenarios and provide a level of confidence associated with their predictions. In this paper, we present results from using Deep Gaussian Process Approximation (DGPA) methods for errant beam prediction at Spallation Neutron Source (SNS) accelerator (classification) and we provide an uncertainty aware surrogate model for the Fermi National Accelerator Lab (FNAL) Booster Accelerator Complex (regression).

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