Detecting anomalies in non-static environments: continual learning applied to CERN's kicker magnet

The CERN accelerator complex relies critically on fast injection and extraction processes to transfer particle beams between accelerators via fast pulsed magnets, or kickers. Ensuring high availability is paramount, as the reliability of these systems directly impacts the large number of experiments conducted at CERN. In this paper, we propose to explore Continual Learning (CL) methods, specifically using Variational Autoencoders (VAEs), to develop an anomaly detection system for the fast kicker magnets. By continuously learning from evolving data while retaining prior knowledge, these models will be capable of detecting anomalies without the need for repeated retraining. This approach is particularly relevant for ensuring the reliability and stability of kicker magnets, where early anomaly detection is critical for preventing performance degradation.