End-to-end Deep Learning Pipeline for Microwave Kinetic Inductance Detector (MKID) Resonator Identification and Tuning
This work addresses a critical bottleneck in astronomical instrumentation by automating the calibration of large MKID arrays, significantly speeding up the process for researchers in astrophysics and related fields.
The authors tackled the time-consuming manual calibration of Microwave Kinetic Inductance Detector arrays by developing an end-to-end deep learning pipeline using a convolutional neural network, achieving equal performance to manual tuning while reducing the time from 4-6 hours to just twelve minutes per feedline.
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.