Document Type

Article

Publication Date

2025

DOI

10.1103/PhysRevAccelBeams.28.034602

Publication Title

Physical Review Accelerators and Beams

Volume

28

Issue

3

Pages

034602 (1-8)

Abstract

We present an unsupervised learning framework for detecting anomalous superconducting radio-frequency (SRF) cavity behavior at the Continuous Electron Beam Accelerator Facility (CEBAF), emphasizing its initial performance and effectiveness. Key to the system’s success was the development of data acquisition systems (DAQs) that capture fast-sampled, information-rich signals, essential for detecting transient effects. The approach involves creating daily cavity-specific models using principal component analysis to handle variations in rf signal behavior and mitigate performance degradation from data drift. This unsupervised method eliminates the need for expensive labeling by continuously updating models with recent data. Deployed and operational for 3 months before a scheduled shutdown, the system successfully identified several issues with DAQ signals, confirming its effectiveness. Despite access to only a fraction of CEBAF’s SRF cavity signals, the framework efficiently detected several instances requiring intervention, demonstrating a significant improvement over traditional, labor-intensive methods of manual plot inspection.

Rights

© 2025 The Authors.

Published under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Further distribution of this work must maintain attribution to the authors and the published article's title, journal citation, and DOI.

Original Publication Citation

Ferguson, H., Li, J., Carpenter, A., Tennant, C., Thomas, D., & Turner, D. (2025). Detecting anomalous SRF cavity behavior with unsupervised learning. Physical Review Accelerators and Beams, 28(3), 1-8, Article 034602. https://doi.org/10.1103/PhysRevAccelBeams.28.034602

ORCID

0000-0001-7078-7468 (Ferguson), 0000-0003-0091-6986 (Li)

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