Anomaly Detection in Particle Accelerators using Autoencoders

The application of machine learning techniques for anomaly detection in particle accelerators has gained popularity in recent years. These efforts have ranged from the analysis of quenches in radio frequency cavities and superconducting magnets to anomalous beam position monitors, and even losses in rings. Using machine learning for anomaly detection can be challenging owing to the inherent imbalance in the amount of data collected during normal operations as compared to during faults. Additionally, the data are not always labeled and therefore supervised learning is not possible. Autoencoders, neural networks that form a compressed representation and reconstruction of the input data, are a useful tool for such situations. Here we explore the use of autoencoder reconstruction analysis for the prediction of magnet faults in the Advanced Photon Source (APS) storage ring at Argonne National Laboratory.