# Deep learning based pulse shape discrimination for germanium detectors

## TL;DR

This paper introduces a machine learning approach combining neural networks for feature extraction and classification to improve background event identification in germanium detectors, enhancing sensitivity in rare process experiments.

## Contribution

A novel machine learning method that efficiently recognizes background events in germanium detectors with minimal labeled data and less calibration effort.

## Key findings

- Matches performance of existing algorithms
- Requires less tuning and calibration
- Potentially identifies background events missed by other methods

## Abstract

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.