# Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator

arXiv: https://arxiv.org/abs/2110.12006 · HTML: https://mlsanity.com/paper/2110.12006/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness](https://mlsanity.com/paper/2006.10108/) (2020)
- [Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory](https://mlsanity.com/paper/2006.06562/) (2020)
- [A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators](https://mlsanity.com/paper/2102.00786/) (2021)
- [Machine learning applied to single-shot x-ray diagnostics in an XFEL](https://mlsanity.com/paper/1610.03378/) (2016)

## Built on by (most cited first, 5 of 5)

- [Machine Learning in Nuclear Physics](https://mlsanity.com/paper/2112.02309/) (2021)
- [Multi-module based CVAE to predict HVCM faults in the SNS accelerator](https://mlsanity.com/paper/2304.10639/) (2023)
- [Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning](https://mlsanity.com/paper/2209.15570/) (2022)
- [Uncertainty Aware Deep Learning for Particle Accelerators](https://mlsanity.com/paper/2309.14502/) (2023)
- [Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology](https://mlsanity.com/paper/2401.15543/) (2024)

## Abstract (arXiv)

High-power particle accelerators are complex machines with thousands of pieces of equipmentthat are frequently running at the cutting edge of technology. In order to improve the day-to-dayoperations and maximize the delivery of the science, new analytical techniques are being exploredfor anomaly detection, classification, and prognostications. As such, we describe the applicationof an uncertainty aware Machine Learning method, the Siamese neural network model, to predictupcoming errant beam pulses using the data from a single monitoring device. By predicting theupcoming failure, we can stop the accelerator before damage occurs. We describe the acceleratoroperation, related Machine Learning research, the prediction performance required to abort beamwhile maintaining operations, the monitoring device and its data, and the Siamese method andits results. These results show that the researched method can be applied to improve acceleratoroperations.