Deep learning based pulse shape discrimination for germanium detectors

Understand

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.