Application of novel machine learning techniques for highly sensitive cryogenic particle detectors
|Programm / Ausschreibung
|FORPA, Forschungspartnerschaften NATS/Ö-Fonds, FORPA OEF2019
|2020 - 2023
|Sensors, Machine Learning, Control Theory, Data Analysis, Cryogenic Detectors
Goal of the project is to employ novel machine learning techniques for highly sensitive cryogenic detectors which are used for the dark matter experiments CRESST and COSINUS and the neutrino experiment NUCLEUS. The project factorizes into two distinct tasks: the aim of the first task is to develop, implement, and study an optimal event classifier and regressor for the experiment.
The second task is an optimal control problem: the operation of the sensors will be optimized by a machine learning algorithm to maximize the sensitivity of the experiments which is driven by the signal-to-noise ratio of the sensors and their stability over time.