Author: Schlarb, H.
Paper Title Page
THPOPA15 Anomaly Detection Based Quench Detection System for CW Operation of SRF Cavities 775
  • G. Martino, A. Bellandi, J. Branlard, A. Eichler, H. Schlarb
    DESY, Hamburg, Germany
  • S. Aderhold, A.L. Benwell, D. Gonnella, S.L. Hoobler, J. Nelson, R.D. Porter, A. Ratti, L.M. Zacarias
    SLAC, Menlo Park, California, USA
  • L.R. Doolittle
    LBNL, Berkeley, California, USA
  • G. Fey
    Hamburg University of Technology, Hamburg, Germany
  Funding: This work is supported by DASHH (Data Science in Hamburg - HELMHOLTZ Graduate School for the Structure of Matter) under Grant No.: HIDSS-0002.
Superconducting radio frequency (SRF) cavities are used in modern particle accelerators to take advantage of their very high quality factor (Q). A higher Q means that a higher RF field can be sustained, and a higher acceleration can be produced in the cavity for length unity. However, in certain situations, e.g., too high RF field, the SRF cavities can experience quenches that risk creating damage due to the rapid increase in the heat load. This is especially negative in continuous wave (CW) operation due to the impossibility of the system to recover during the off-load period. The design goal of a quench-detection system is to protect the system without being a limiting factor during the operation. In this paper, we compare two different classification approaches for improving a quench detection system. We perform tests using traces recorded from LCLS-II and show that the ARSENAL classifier outperforms a CNN classifier in terms of accuracy.
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About • Received ※ 24 August 2022 — Accepted ※ 25 August 2022 — Issue date ※ 23 September 2022  
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