A fast machine learning based algorithm for MKID readout power tuning
ISSTT 2017 - 28th International Symposium on Space Terahertz Technology 2017-March (2017)
Abstract:
As high pixel count Microwave Kinetic Inductance Detector (MKID) arrays become widely adopted, there is a growing demand for automated device readout calibration. These calibrations include ascertaining the optimal driving power for best pixel sensitivity, which, because of large variations in MKID behavior, is typically performed by manual inspection. This process takes roughly 1 hour per 1000 MKIDs, making the manual characterization of ten-kilopixel scale arrays unfeasible. We propose the concept of using a machine-learning algorithm, based on a convolution neural network (CNN) architecture, which should reliably tune ten-kilopixel scale MKID arrays on the order of several minutes.Exoplanet atmospheres with EChO: spectral retrievals using EChOSim
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