Martino, GianlucaGianlucaMartinoBellandi, AndreaAndreaBellandiEichler, AnnikaAnnikaEichlerBranlard, JulienJulienBranlardSchlarb, HolgerHolgerSchlarbDoolittle, Lawrence R.Lawrence R.DoolittleAderhold, SebastianSebastianAderholdHoobler, SonyaSonyaHooblerNelson, Janice A.Janice A.NelsonPorter, Ryan D.Ryan D.PorterZacarias, L.L.ZacariasBenwell, Andrew L.Andrew L.BenwellGonnella, Dan A.Dan A.GonnellaRatti, AlessandroAlessandroRattiFey, GörschwinGörschwinFey2024-06-052024-06-052022-01-0131st International Linear Accelerator Conference (LINAC 2022)[9783954502158]https://hdl.handle.net/11420/47750Superconducting 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.enConference Paper10.18429/JACoW-LINAC2022-THPOPA15Conference Paper