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Description
The LEGEND experiment aims to detect neutrinoless double-beta ($0\nu\beta\beta$) decay using high-purity germanium detectors (HPGes) enriched in $^{76}$Ge, immersed in instrumented liquid argon (LAr). Atmospheric LAr contains the cosmogenically activated isotope $^{42}$Ar, whose decay progeny, $^{42}$K, can undergo beta decay ($Q_{\beta} = 3.5$ MeV) on the HPGe surface. Without the baseline mitigation strategy—using underground-sourced LAr (UGLAr) depleted in $^{42}$Ar—this decay would become the dominant background at the $0\nu\beta\beta$ Q-value ($Q_{\beta\beta} = 2.039$ MeV) in LEGEND-1000. Given the non-negligible risk that UGLAr may not be available in time for LEGEND-1000, alternative approaches are being explored, such as optically active enclosures combined with machine-learning-based pulse-shape discrimination (ML-PSD), to distinguish between $0\nu\beta\beta$ signals and background events. To develop and evaluate novel ML-PSD techniques, we operated high-purity germanium (HPGe) detectors in $^{42}$Ar-enriched LAr at the SCARF LAr test facility at TU Munich, generating a dataset enriched in $^{42}$K surface beta events.
In this work, we investigate the construction of a latent representation of raw HPGe waveform data using variational inference. Unlike conventional PSD parameters, the latent vectors are designed to fully utilize the high-level features of the waveforms. By constraining the latent space with a predefined prior, we estimate the data density corresponding to a signal proxy derived from $^{228}$Th calibration data. This is achieved by first employing a classifier neural network to estimate the posterior probability of class labels for samples drawn from both the latent prior and the signal-proxy distribution and then applying Bayes’ rule to compute the likelihood of the data under the signal-like hypothesis.
We use the resulting density estimate to classify events as signal- or background-like at a specified significance level. Our evaluation demonstrates promising suppression of $^{42}$K surface beta events, providing a pathway for density-based PSD that utilizes the complete raw waveform information from HPGe detectors.
This work was supported by the Cluster of Excellence ORIGINS (EXC 2094-39078331), funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy, and by the DFG Collaborative Research Center SFB1258-283604770.
| AI keywords | variational inference, anomaly detection, pattern recognition |
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