  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.
