Conveners
Anomaly Detection and Diagnostics
- Annika Eichler (DESY)
Generative phase space reconstruction method based on neural networks and differentiable simulations has become a novel beam diagnostic technique to obtain the beam phase space information. Recent studies show that four-dimensional phase space can be successfully obtained by using only YAG images with different quadrupole magnet strength, allowing us to understand both uncoupled and coupled...
Coherent synchrotron radiation (CSR) is a limiting effect in linear accelerators with dispersive elements due to its contribution to projected transverse emittance growth. This effect becomes a limitation for highly compressed beams. Even though CSR-induced projected emittance growth has been widely studied, conventional measurement techniques are not detailed enough to resolve the...
Optics tuning in transfer lines and LINACs can be challenging due to the fact that multiple combinations of machine settings can lead to the same diagnostic output. Moreover, the lack of a periodic solution can limit the ability to infer optics in the same way as rings from BPM signals. Model based approaches are often used to assist with the optics tuning in combination with optimization or...
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...
At the European XFEL, detecting anomalies in superconducting cavities is essential for reliable accelerator performance. We began with a model-based fault detection approach focused on residual analysis to identify anomalies. To improve fault discrimination, particularly for quench events, we augmented this system with machine learning (ML) models. Key challenges included the scarcity of...
The vast amount of data generated by accelerators makes manual monitoring impractical due to its labor-intensive nature. Existing machine learning solutions often rely on labeled data, manual inspection, and hyperparameter tuning, which limits their scalability. To address these challenges, we leverage coincidence learning—an unsupervised technique designed for multi-modal tasks—to...
