  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 DARKNESS (DARK-speckle Near-infrared Energy-resolving
Superconducting Spectrophotometer) and MEC (MKID Exoplanet Camera), contain
10-20,000 pixels, making the calibration process extremely time consuming; each
2000 pixel feedline requires 4-6 hours of manual tuning. Here we present a
pipeline which uses a single convolutional neural network (CNN) 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
twelve minutes of computational time per feedline.
