5th ICFA Beam Dynamics Mini-Workshop on Machine Learning for Particle Accelerators

Anomaly Forecasting and Adaptive Learning in Fast Kicker Magnet Systems- 15'+5'

10 Apr 2025, 09:30

20m

503/1-001 - Council Chamber (CERN)

Invited talks Anomaly Detection and Diagnostics Anomaly Detection and Diagnostics

Speaker

Malik Marco Algelly (Universite de Geneve (CH))

Description

Kicker magnets are essential for particle beam injection and extraction within CERN’s accelerator complex, where high reliability is crucial to maintaining the availability needed for numerous scientific experiments. This study proposes a machine learning approach for forecasting anomalies in these systems, aiming to proactively identify and isolate potential faults before failure occurs. To keep the anomaly detection model accurate over time, continual learning techniques are employed, allowing the model to adapt to evolving system dynamics without frequent retraining. This combination enhances the efficiency and stability of accelerator operations by ensuring the model remains up-to-date in the face of non-static data.

Presentation materials

2025_ICFA_Algelly.pdf

2025_ICFA_Algelly.pptx