Deep learning based pulse shape discrimination for germanium detectors
This addresses background reduction for experiments in particle physics, but it is incremental as it builds on existing methods with improvements in training efficiency and calibration.
The paper tackles the problem of identifying background events in rare-process experiments like neutrinoless double beta decay by developing a novel machine learning method for pulse shape discrimination in germanium detectors. The result shows that the method matches state-of-the-art performance, requires less tuning, and has potential to identify missed background events.
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.