  Experiments searching for rare processes like neutrinoless double beta decay
heavily rely on the identification of background events to reduce their
background level and increase their sensitivity. We present a novel machine
learning based method to recognize one of the most abundant classes of
background events in these experiments. By combining a neural network for
feature extraction with a smaller classification network, our method can be
trained with only a small number of labeled events. To validate our method, we
use signals from a broad-energy germanium detector irradiated with a $^{228}$Th
gamma source. We find that it matches the performance of state-of-the-art
algorithms commonly used for this detector type. However, it requires less
tuning and calibration and shows potential to identify certain types of
background events missed by other methods.
