Speaker
Description
With metallic-magnetic calorimeters (MMCs) - like the maXs-detector series developed within this collaboration - promising new tools for high precision x-ray spectroscopy application have become available. Because of their unique working principles, MMCs combine several advantages over conventional energy- and wavelength-dispersive photon detectors. They can reach spectral resolving powers of up to
During several successful benchmark experiments [3-6] a comprehensive signal analysis software framework was developed. Though, setting up the detectors and analyzing their complex behavior involves a multitude of numerical values and hardware settings to be optimized in the process. This also requires several manual steps which becomes increasingly more difficult to manage with a growing number of pixels per detector. Therefore, the usage of artificial intelligence to help with the simplification of the process and a possible improvement of the results is planned for future investigation. Starting with a simple peak characterization for a more precise identification of false-positive trigger events, up to more demanding tasks like an auto-tuning procedure to optimize the various setting of the SQUID read-out per pixel, MMC operation gives rise to a plentitude of opportunities to utilize novel AI technologies. In this work we will present our first steps and future plans regarding potential synergies between our quantum sensor technologies and AI-based algorithms for fundamental atomic physics research.
