1–6 Jun 2025
Taipei International Convention Center (TICC)
Asia/Taipei timezone

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

THPM112
5 Jun 2025, 15:30
2h
Exhibiton Hall A _Magpie (TWTC)

Exhibiton Hall A _Magpie

TWTC

Poster Presentation MC6.D13 Machine Learning Thursday Poster Session

Speaker

Malik Algelly (European Organization for Nuclear Research)

Description

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.

Region represented Europe
Paper preparation format LaTeX

Author

Malik Algelly (European Organization for Nuclear Research)

Co-authors

Francesco Velotti (European Organization for Nuclear Research) Konstantinos Papastergiou (European Organization for Nuclear Research) Patrick Ellison (European Organization for Nuclear Research) Verena Kain (European Organization for Nuclear Research)

Presentation materials

There are no materials yet.