Detector development for the CRESST experiment

Recently low-mass dark matter direct searches have been hindered by a low energy background, drastically reducing the physics reach of the experiments. In the CRESST-III experiment, this signal is characterised by a significant increase of events below 200 eV. As the origin of this background is still unknown, it became necessary to develop new detector designs to reach a better understanding of the observations. Within the CRESST collaboration, three new different detector layouts have been developed and they are presented in this contribution.Comment: 8 pages, 4 figure

Observation of a low energy nuclear recoil peak in the neutron calibration data of the CRESST-III Experiment

New-generation direct searches for low mass dark matter feature detection thresholds at energies well below 100 eV, much lower than the energies of commonly used X-ray calibration sources. This requires new calibration sources with sub-keV energies. When searching for nuclear recoil signals, the calibration source should ideally cause mono-energetic nuclear recoils in the relevant energy range. Recently, a new calibration method based on the radiative neutron capture on $^{182}$W with subsequent de-excitation via single $\gamma$-emission leading to a nuclear recoil peak at 112 eV was proposed. The CRESST-III dark matter search operated several CaWO$_{4}$-based detector modules with detection thresholds below 100 eV in the past years. We report the observation of a peak around the expected energy of 112 eV in the data of three different detector modules recorded while irradiated with neutrons from different AmBe calibration sources. We compare the properties of the observed peaks with Geant-4 simulations and assess the prospects of using this for the energy calibration of CRESST-III detectors.Comment: 8 pages, 4 figures; submitted to Phys. Rev.

Testing spin-dependent dark matter interactions with lithium aluminate targets in CRESST-III

In the past decades, numerous experiments have emerged to unveil the nature of dark matter, one of the most discussed open questions in modern particle physics. Among them, the CRESST experiment, located at the Laboratori Nazionali del Gran Sasso, operates scintillating crystals as cryogenic phonon detectors. In this work, we present first results from the operation of two detector modules which both have 10.46 g LiAlO$_2$ targets in CRESST-III. The lithium contents in the crystal are $^6$Li, with an odd number of protons and neutrons, and $^7$Li, with an odd number of protons. By considering both isotopes of lithium and $^{27}$Al, we set the currently strongest cross section upper limits on spin-dependent interaction of dark matter with protons and neutrons for the mass region between 0.25 and 1.5 GeV/c$^2$.Comment: 9 pages, 8 figure

High-Dimensional Bayesian Likelihood Normalisation for CRESST's Background Model

Using CaWO$_4$ crystals as cryogenic calorimeters, the CRESST experiment searches for nuclear recoils caused by the scattering of potential Dark Matter particles. A reliable identification of a potential signal crucially depends on an accurate background model. In this work we introduce an improved normalisation method for CRESST's model of the electromagnetic backgrounds. Spectral templates, based on Geant4 simulations, are normalised via a Bayesian likelihood fit to experimental background data. Contrary to our previous work, no assumption of partial secular equilibrium is required, which results in a more robust and versatile applicability. Furthermore, considering the correlation between all background components allows us to explain 82.7% of the experimental background within [1 keV, 40 keV], an improvement of 18.6% compared to our previous method.Comment: 24 pages, 14 figures, submitted to EPJ

Optimal operation of cryogenic calorimeters through deep reinforcement learning

Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to light dark matter-nucleus scattering in current direct detection dark matter searches. 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 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.Comment: 23 pages, 14 figures, 2 table

Results on sub-GeV Dark Matter from a 10 eV Threshold CRESST-III Silicon Detector

We present limits on the spin-independent interaction cross section of dark matter particles with silicon nuclei, derived from data taken with a cryogenic calorimeter with 0.35 g target mass operated in the CRESST-III experiment. A baseline nuclear recoil energy resolution of $(1.36\pm 0.05)$ eV$_{\text{nr}}$, currently the lowest reported for macroscopic particle detectors, and a corresponding energy threshold of $(10.0\pm 0.2)$ eV$_{\text{nr}}$ have been achieved, improving the sensitivity to light dark matter particles with masses below 160 MeV/c$^2$ by a factor of up to 20 compared to previous results. We characterize the observed low energy excess, and we exclude noise triggers and radioactive contaminations on the crystal surfaces as dominant contributions.Comment: 8 pages, 5 figures; precised the position of the calibration source in Fig. 1; extended the discussion about the observed energy spectrum; added the DM limit curve to ancillary files. Published in Phys. Rev.

Towards an automated data cleaning with deep learning in CRESST

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.Comment: 12 pages, 8 figures, 6 table

Latest observations on the low energy excess in CRESST-III

The CRESST experiment observes an unexplained excess of events at low energies. In the current CRESST-III data-taking campaign we are operating detector modules with different designs to narrow down the possible explanations. In this work, we show first observations of the ongoing measurement, focusing on the comparison of time, energy and temperature dependence of the excess in several detectors. These exclude dark matter, radioactive backgrounds and intrinsic sources related to the crystal bulk as a major contribution.Comment: 10 pages, 5 figures; to be published in IDM2022 proceeding