End-to-end deep learning pipeline for microwave kinetic inductance detector resonator identification and tuning

Abstract

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 exoplanet camera, contain 10 to 20,000 pixels, making the calibration process extremely time-consuming; each 2000-pixel feedline requires 4 to 6 h of manual tuning. We present a pipeline that uses a single convolutional neural network to perform both resonator identification and tuning simultaneously. We find that our pipeline has performance equal to that of the manual tuning process and requires just 12 min of computational time per feedline.