Speakers
Description
The MeV gamma-ray domain suffers from poor sensitivity, motivating advanced detector and readout technologies. The i-RASE (Intelligent RAdiation SEnsor readout systems) project addresses this by combining a custom low-noise front end with FPGA-implemented, physics-informed neural networks for real-time event reconstruction in 3D CdZnTe (CZT) Drift-Strip Detectors (DSD). At DTU Space, work focuses on 3D CZT DSD design specifications, development of a physics-based detector model, and experimental validation. The framework integrates COMSOL signal simulations and Geant4 charge-cloud generation to optimize detector geometry and generate synthetic training data. Laboratory tests show that neural-network reconstruction achieves sub-millimetre FWHM spatial resolution at 662 keV, matching or exceeding conventional methods, and reduces dead zones by improving boundary position estimates.