Explainable machine learning for breakdown prediction in high gradient rf cavities
نویسندگان
چکیده
The occurrence of vacuum arcs or radio frequency (rf) breakdowns is one the most prevalent factors limiting high-gradient performance normal conducting rf cavities in particle accelerators. In this paper, we search for existence previously unrecognized features related to incidence by applying a machine learning strategy cavity data from CERN's test stand Compact Linear Collider (CLIC). By interpreting parameters learned models with explainable artificial intelligence (AI), reverse-engineer physical properties deriving fast, reliable, and simple rule-based models. Based on 6 months historical dedicated experiments, our show fractions high influence breakdowns. Specifically, it shown that field emitted current following an initial breakdown closely probability another occurring shortly thereafter. Results also indicate pressure should be monitored increased temporal resolution future further explore activity associated
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ژورنال
عنوان ژورنال: Physical review accelerators and beams
سال: 2022
ISSN: ['2469-9888']
DOI: https://doi.org/10.1103/physrevaccelbeams.25.104601