Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities. (arXiv:2202.05610v1 [physics.acc-ph])

Radio Frequency (RF) breakdowns are one of the most prevalent limiting
factors in RF cavities for particle accelerators. During a breakdown, field
enhancement associated with small deformations on the cavity surface results in
electrical arcs. Such arcs lead to beam aborts, reduce machine availability and
can cause irreparable damage on the RF cavity surface. In this paper, we
propose a machine learning strategy to discover breakdown precursors in CERN's
Compact Linear Collider (CLIC) accelerating structures. By interpreting the
parameters of …

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