# End-to-end Deep Learning Pipeline for Microwave Kinetic Inductance Detector (MKID) Resonator Identification and Tuning

@article{Fruitwala2021EndtoendDL, title={End-to-end Deep Learning Pipeline for Microwave Kinetic Inductance Detector (MKID) Resonator Identification and Tuning}, author={Neelay Fruitwala and Alex B. Walter and John I Bailey and Rupert Dodkins and Benjamin A. Mazin}, journal={ArXiv}, year={2021}, volume={abs/2104.01282} }

We present the development of a machine learning-based pipeline to fully automate the calibration of the frequency comb used to read out optical/IR microwave kinetic inductance detector (MKID) arrays. This process involves determining the resonant frequency and optimal drive power of every pixel (i.e., resonator) in the array, which is typically done manually. Modern optical/IR MKID arrays, such as the DARK-Speckle Near-Infrared Energy-Resolving Superconducting Spectrophotometer and the MKID… Expand

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