Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory

Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory

We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate elec...

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Bibliographic Details
Main Authors: Chris Tennant, Adam Carpenter, Tom Powers, Anna Shabalina Solopova, Lasitha Vidyaratne, Khan Iftekharuddin
Format: Article
Language:English
Published: American Physical Society 2020-11-01
Series:Physical Review Accelerators and Beams
Online Access:http://doi.org/10.1103/PhysRevAccelBeams.23.114601

Internet

http://doi.org/10.1103/PhysRevAccelBeams.23.114601

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