Machine Learning-based Anomaly Detection for Particle Accelerators

Particle accelerators are complex systems composed of multiple subsystems that must work together to produce high quality beams employed for physics experiments. A fault or an anomalous behaviour in one of such subsystems can lead to expensive downtime for the whole facility. Thus, it is of paramount importance to be able to promptly detect anomalies. Given the vast amount of streaming data generated by accelerator field sensors, Machine Learning (ML)-based tools are promising candidates for efficient monitoring of such systems: an approach based on unsupervised ML techniques exploiting the data from a Radio Frequency tuning system is here proposed. Feature importance is exploited to guide the definition of the optimal windowing for feature extraction. The proposed approach is here validated on real-world data related to the ALPI accelerator at Legnaro National Laboratories in Italy.