Data Augmentation for Breakdown Prediction in CLIC RF Cavities

One of the primary limitations on the achievable accelerating gradient in normal-conducting accelerator cavities
is the occurrence of vacuum arcs, also known as RF breakdowns. A recent study on experimental data from the CLIC
XBOX2 test stand at CERN proposes the use of supervised
machine learning methods for predicting RF breakdowns.
As RF breakdowns occur relatively infrequently during operation, the majority of the data was instead comprised of
non-breakdown pulses. This phenomenon is known in the
field of machine learning as class imbalance and is problematic for the training of the models. This paper proposes
the use of data augmentation methods to generate synthetic
data to counteract this problem. Different data augmentation
methods like random transformations and pattern mixing
are applied to the experimental data from the XBOX2 test
stand, and their efficiency is compared