Optimal Operation of Cryogenic Calorimeters Through Deep Reinforcement Learning.

Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to sub-GeV/c 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.

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.

Secular equilibrium assessment in a CaWO target crystal from the dark matter experiment CRESST using Bayesian likelihood normalisation.

CRESST is a leading direct detection sub-GeVc dark matter experiment. During its second phase, cryogenic bolometers were used to detect nuclear recoils off the CaWO target crystal nuclei. The previously established electromagnetic background model relies on Secular Equilibrium (SE) assumptions.

Natural radionuclides as background sources in the Modane underground laboratory.

The Modane underground laboratory (LSM) is the deepest operating underground laboratory in Europe. It is located under the Fréjus peak in Savoie Alps in France, with average overburden of 4800 m w. e.

Geant4-based electromagnetic background model for the CRESST dark matter experiment.

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 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.