# Uncertainty Aware Deep Learning for Particle Accelerators

arXiv: https://arxiv.org/abs/2309.14502 · HTML: https://mlsanity.com/paper/2309.14502/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness](https://mlsanity.com/paper/2006.10108/) (2020)
- [Uncertainty Quantification and Deep Ensembles](https://mlsanity.com/paper/2007.08792/) (2020)
- [BOOSTR: A Dataset for Accelerator Control Systems](https://mlsanity.com/paper/2101.08359/) (2021)
- [Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator](https://mlsanity.com/paper/2110.12006/) (2021)

## Built on by (most cited first, 2 of 2)

- [Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators](https://mlsanity.com/paper/2312.10040/) (2023)
- [Uncertainty Guided Online Ensemble for Non-stationary Data Streams in Fusion Science](https://mlsanity.com/paper/2511.02092/) (2025)

## Abstract (arXiv)

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).