Oct 9 – 13, 2022
FHNW Campus Brugg-Windisch
Europe/Zurich timezone

Machine Learning assisted Cavity Quench Identification at the European XFEL

Oct 10, 2022, 2:15 PM
Aula (Building 3)


Building 3

Oral Low Level RF Workshop 2022 Machine Learning


Julien Branlard (DESY)


A server-based quench detection system is used since the beginning of operation at the European XFEL (2017) to stop driving superconducting cavities if they experience a quench. While this approach effectively detects quenches, it also generates false positives, tripping the accelerating stations when failures other than quenches occur. Using the post-mortem data snapshots generated for every trip, an additional signal (referred to as residual) is systematically computed based on the standard cavity model. Following an initial training on a subset of such residuals previously tagged as “quench” / “non-quench”, two independent machine learning engines analyze routinely the trip snapshots and their residuals to identify if a trip was indeed triggered by a quench or has another root cause. The outcome of the analysis is automatically appended to the data snapshots and distributed to a team of experts. This constitutes a fully deployed example of machine-learning-assisted failure classification to identify quenches, supporting experts in their daily routine of monitoring and documenting the accelerator uptime and availability.

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