Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology
It addresses the need for automated fault detection in accelerator operations to reduce manual intervention, though it is incremental as it applies an existing method to a new domain.
This paper tackles the problem of detecting anomalies in particle accelerator orbit lock systems by developing an unsupervised LSTM Auto Encoder method, achieving 68.6%-89.3% detection rates and up to 82% accuracy on data from CEBAF.
A stable, reliable, and controllable orbit lock system is crucial to an electron (or ion) accelerator because the beam orbit and beam energy instability strongly affect the quality of the beam delivered to experimental halls. Currently, when the orbit lock system fails operators must manually intervene. This paper develops a Machine Learning based fault detection methodology to identify orbit lock anomalies and notify accelerator operations staff of the off-normal behavior. Our method is unsupervised, so it does not require labeled data. It uses Long-Short Memory Networks (LSTM) Auto Encoder to capture normal patterns and predict future values of monitoring sensors in the orbit lock system. Anomalies are detected when the prediction error exceeds a threshold. We conducted experiments using monitoring data from Jefferson Lab's Continuous Electron Beam Accelerator Facility (CEBAF). The results are promising: the percentage of real anomalies identified by our solution is 68.6%-89.3% using monitoring data of a single component in the orbit lock control system. The accuracy can be as high as 82%.