Dynamic noise estimation approach for X-ray detectors on FPGAs

The proposed algorithm enables real-time noise estimation directly on the raw data stream of an X-ray detection system. This allows for better detector monitoring and increases the measurement accuracy, especially in long term measurements and under changing environment conditions. The noise value is used as an event extraction threshold and is therefore the most critical parameter for the performance in X-ray data processing. Inaccurately estimated noise values can result into significantly poorer energy resolutions. The new algorithm is designed for low memory bandwidth, precise estimation results and a low processing complexity. This allows real-time data processing even for large and fast detector systems with an output rate of hundreds of MPixels/s. Compared to the calculation method used up to now the memory bandwidth can be reduced down to a factor of a hundred with the new algorithm. The new developed real-time Dynamic Noise Estimation (DNE) algorithm uses an iterative approach for the estimation. For the calculation is only an increment/decrement operation and an integer comparison required. This simple approach allows a resource efficient hardware implementation on FPGAs. Simulation results confirm the excellent estimation accuracy and stability with real-time performance. Measurements with Fano-limited energy resolution, the theoretically best achievable energy resolution with semiconductor detectors, confirm this outstanding performance on real detector systems. The reliability and stability of the system was demonstrated during a long term irradiation campaign for Mercury Imaging X-ray Spectrometer (MIXS). MIXS is an instrument onboard ESA's corner stone mission BepiColombo and will be launched 2015 into an orbit around Mercury.