Work

Pulse shape discrimination for event rejection in BEGe-type high-purity germanium detectors

Year: 2026
Type: article
Abstract: High-purity germanium detectors are widely used in rare-event searches due to their excellent energy resolution and extremely high intrinsic (radio)purity. In experiments searching for neutrinoless double beta decay in $$^{76}$$ 76 Ge such as Legend , pulse shape discrimination is required to suppress multi-site $$\gamma $$ γ events. In this work, we investigate whether pulse shape discrimination classifiers trained exclusively on $$\gamma $$ γ ray data can be used to identify and reject $$\alpha $$ α events, without the need for dedicated $$\alpha $$ α training. In detectors such as Legend , the total number of registered $$\alpha $$ α events over the experiment lifetime is expected to be insufficient to train dedicated classifiers, while still contributing to the background. Two approaches based on machine learning are studied: a multilayer perceptron and a projective likelihood classifier. The p+ surface of a point-contact semi-planar germanium detector was exposed to $$^{209}$$ 209 Po and $$^{210}$$ 210 Po sources deposited on a thin gold foil. Two measurement campaigns were performed, yielding $$1.36\times 10^{5}$$ 1.36 × 10 5 and $$1.87\times 10^{6}$$ 1.87 × 10 6 $$\alpha $$ α events, respectively. Both classification methods achieve efficient separation of single-site and multi-site $$\gamma $$ γ events while strongly reducing the $$\alpha $$ α component. The multilayer perceptron provides the best overall performance, with a signal-like event survival greater than 80%, a background-like event survival below 20%, and an $$\alpha $$ α -rejection factor exceeding $$2.71\times 10^{4}$$ 2.71 × 10 4 . These results demonstrate that robust pulse shape discrimination for high-purity germanium detectors can be achieved using training information derived solely from $$\gamma $$ γ events, providing a promising strategy for next-generation neutrinoless double beta decay searches.
Language: English
Open Access status: diamond
OpenAlex ID: W7223347816
FWCI:
Cites: 19
Cited by:
Related to: 10