Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities

TL;DR

This study employs explainable machine learning to identify physical features associated with rf breakdowns in high-gradient cavities, enabling better prediction and understanding of breakdown phenomena in particle accelerators.

Contribution

The paper introduces a novel explainable AI approach to analyze cavity data, revealing physical insights and proposing simple rule-based models for breakdown prediction.

Findings

Field emitted current after breakdown correlates with subsequent breakdown probability.

Monitoring cavity pressure with higher temporal resolution could improve breakdown understanding.

Models identify specific data fractions influencing breakdown occurrence.

Abstract

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