Search

Cryogenic detectors can detect the smallest energy depositions via the scattering of the incoming particle with the detector material. The deposited energy leads to minimal temperature rises of a few [Formula: see text], read out via transition edge sensors and SQUIDs. Using scintillating crystals as detector material offers the possibility of discriminating between nuclear recoils from dark matter scattering and electromagnetic background events. The CRESST experiment pioneered this technology and is still among the most sensitive experiments searching for sub-GeV dark matter particles. The technology is now also used by other experiments for dark matter searches (COSINUS) and for measuring coherent elastic neutrino-nucleus scattering (NUCLEUS). We discuss cryogenic detectors' detection principle and their application. We present the latest dark matter results from CRESST, a new type of background, and the status of the COSINUS and NUCLEUS experiments. This article is part of the theme issue 'The particle-gravity frontier'.

Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to sub-GeV/c 2 dark matter interactions with nuclei in current direct detection experiments. In such devices, the temperature of the thermometer and the bias current in its readout circuit need careful optimization to achieve optimal detector performance. This task is not trivial and is typically done manually by an expert. In our work, we automated the procedure with reinforcement learning in two settings. First, we trained on a simulation of the response of three Cryogenic Rare Event Search with Superconducting Thermometers (CRESST) detectors used as a virtual reinforcement learning environment. Second, we trained live on the same detectors operated in the CRESST underground setup. In both cases, we were able to optimize a standard detector as fast and with comparable results as human experts. Our method enables the tuning of large-scale cryogenic detector setups with minimal manual interventions., Competing Interests: Competing interestsOn behalf of all authors, the corresponding author states that there is no conflict of interest. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request., (© The Author(s) 2024.)

CRESST is a leading direct detection sub-GeVc -2 dark matter experiment. During its second phase, cryogenic bolometers were used to detect nuclear recoils off the CaWO 4 target crystal nuclei. The previously established electromagnetic background model relies on Secular Equilibrium (SE) assumptions. In this work, a validation of SE is attempted by comparing two likelihood-based normalisation results using a recently developed spectral template normalisation method based on Bayesian likelihood. Albeit we find deviations from SE in some cases we conclude that these deviations are artefacts of the fit and that the assumptions of SE is physically meaningful., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The CRESST experiment employs cryogenic calorimeters for the sensitive measurement of nuclear recoils induced by dark matter particles. The recorded signals need to undergo a careful cleaning process to avoid wrongly reconstructed recoil energies caused by pile-up and read-out artefacts. We frame this process as a time series classification task and propose to automate it with neural networks. With a data set of over one million labeled records from 68 detectors, recorded between 2013 and 2019 by CRESST, we test the capability of four commonly used neural network architectures to learn the data cleaning task. Our best performing model achieves a balanced accuracy of 0.932 on our test set. We show on an exemplary detector that about half of the wrongly predicted events are in fact wrongly labeled events, and a large share of the remaining ones have a context-dependent ground truth. We furthermore evaluate the recall and selectivity of our classifiers with simulated data. The results confirm that the trained classifiers are well suited for the data cleaning task., Competing Interests: Conflict of interestOn behalf of all authors, the corresponding author states that there is no conflict of interest., (© The Author(s) 2023.)

The CRESST (Cryogenic Rare Event Search with Superconducting Thermometers) dark matter search experiment aims for the detection of dark matter particles via elastic scattering off nuclei in CaWO 4 crystals. To understand the CRESST electromagnetic background due to the bulk contamination in the employed materials, a model based on Monte Carlo simulations was developed using the Geant4 simulation toolkit. The results of the simulation are applied to the TUM40 detector module of CRESST-II phase 2. We are able to explain up to ( 68 ± 16 ) % of the electromagnetic background in the energy range between 1 and 40 keV ., (© The Author(s) 2019.)