# Deep learning based pulse shape discrimination for germanium detectors

arXiv: https://arxiv.org/abs/1903.01462 · HTML: https://mlsanity.com/paper/1903.01462/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2019.

## Built on by (most cited first, 6 of 6)

- [Machine Learning in the Search for New Fundamental Physics](https://mlsanity.com/paper/2112.03769/) (2021)
- [Machine Learning in Nuclear Physics](https://mlsanity.com/paper/2112.02309/) (2021)
- [Interpretable Boosted Decision Tree Analysis for the Majorana Demonstrator](https://mlsanity.com/paper/2207.10710/) (2022)
- [Digital Signal Analysis based on Convolutional Neural Networks for Active Target Time Projection Chambers](https://mlsanity.com/paper/2202.12941/) (2022)
- [Data-driven detector signal characterization with constrained bottleneck autoencoders](https://mlsanity.com/paper/2203.04604/) (2022)
- [Restoring Original Signal From Pile-up Signal using Deep Learning](https://mlsanity.com/paper/2304.14496/) (2023)

## Abstract (arXiv)

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 228Th 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.