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Abstract The detection of individual photons at cryogenic temperatures is of interest to many experiments searching for physics beyond the Standard Model. Silicon photomultipliers (SiPMs) are often deployed in liquid argon or liquid xenon to detect scintillation light either directly or after it has been wavelength-shifted. Maximizing the photon detection efficiency (PDE) of the SiPMs used in these experiments optimizes the sensitivity to new physics; however, the PDEs of commercial SiPMs, although well known at room temperature, are not well characterized at the cryogenic temperatures at which many experiments operate them. Here we present results from an experimental setup that measures the photon detection efficiencies of silicon photomultipliers at liquid nitrogen temperature, 77 K. Results from a KETEK PM3325-WB-D0 and a Hamamatsu S13360-3050CS silicon photomultiplier — of R&D interest to the LEGEND experiment — exhibit a decrease in photon detection efficiency greater than 20% at liquid nitrogen temperature relative to room temperature for 562 nm light. 

Abstract Neutrinoless double-beta decay ( $0\nu \beta \beta$) is a rare nuclear process that, if observed, will provide insight into the nature of neutrinos and help explain the matter-antimatter asymmetry in the Universe. The large enriched germanium experiment for neutrinoless double-beta decay (LEGEND) will operate in two phases to search for $0\nu \beta \beta$. The first (second) stage will employ 200 (1000) kg of High-Purity Germanium (HPGe) enriched in⁷⁶Ge to achieve a half-life sensitivity of 10²⁷(10²⁸) years. In this study, we present a semi-supervised data-driven approach to remove non-physical events captured by HPGe detectors powered by a novel artificial intelligence model. We utilize affinity propagation to cluster waveform signals based on their shape and a support vector machine to classify them into different categories. We train, optimize, and test our model on data taken from a natural abundance HPGe detector installed in the Full Chain Test experimental stand at the University of North Carolina at Chapel Hill. We demonstrate that our model yields a maximum sacrifice of physics events of ${0.024}_{-0.003}^{+0.004}\mathrm{\%}$after data cleaning. Our model is being used to accelerate data cleaning development for LEGEND-200 and will serve to improve data cleaning procedures for LEGEND-1000. 

Abstract The LEGEND collaboration has been developing a⁷⁶Ge-based double-beta decay experimental program where precise radiopurity measurements of ultraclean materials are crucial. Ultralow concentrations of thorium and uranium, the main contributors to the detector background via their decay products, can be determined by inductively coupled plasma mass spectrometry (ICPMS) and accelerator mass spectrometry (AMS). Here we shall present recent developments in thorium and uranium mass spectrometry methods, together with basics of separation chemistry applied to process different samples. The new possibilities to measure²³²Th and²³⁸U by ICPMS and AMS at the Comenius University in Bratislava are discussed as well. 

Abstract Terrestrial and extraterrestrial radioisotope research has been strongly dependent on the development of analytical methods which would enable to trace radioisotopes at low concentrations in subgram samples (e.g., in tree rings, ice cores, meteorites, etc.). Accelerator mass spectrometry (AMS) has become the most sensitive technique for ultralow-level analysis of long-lived radioisotopes, such as¹⁴C,¹⁰Be and²⁶Al. We review developments and applications carried out in the CENTA laboratory, and describe a recently installed fully equipped AMS line, designed for analysis of long-lived radioisotopes from tritium to curium. 

Abstract The impurity density in high-purity germanium detectors is crucial to understand and simulate such detectors. However, the information about the impurities provided by the manufacturer, based on Hall effect measurements, is typically limited to a few locations and comes with a large uncertainty. As the voltage dependence of the capacitance matrix of a detector strongly depends on the impurity density distribution, capacitance measurements can provide a path to improve the knowledge on the impurities. The novel method presented here uses a machine-learned surrogate model, trained on precise GPU-accelerated capacitance calculations, to perform full Bayesian inference of impurity distribution parameters from capacitance measurements. All steps use open-source Julia software packages. Capacitances are calculated with SolidStateDetectors.jl , machine learning is done with Flux.jl and Bayesian inference performed using BAT.jl . The capacitance matrix of a detector and its dependence on the impurity density is explained and a capacitance bias-voltage scan of an n -type true-coaxial test detector is presented. The study indicates that the impurity density of the test detector also has a radial dependence. 

Abstract The analysis of the time profile of electrical signals produced by energy depositions in germanium detectors allows discrimination of events with different topologies. This is especially relevant for experiments searching for the neutrinoless double beta decay of $$^{76}$$ 76 Ge to distinguish the sought-after signal from other background sources. The standard calibration procedures used to tune the selection criteria for double-beta decay events use a $$^{228}$$ 228 Th source, because it provides samples of signal-like events. These samples exhibit energy spatial distributions with subtle different topologies compared to neutrinoless double-beta decay events. In this work, we will characterize these topological differences and, with the support of a $$^{56}$$ 56 Co source, evaluate biases and precision of calibration techniques which use such event samples. Our results will be particularly relevant for future experiments in which a solid estimation of the efficiency is required.