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

TL;DR

This paper introduces an automated machine learning pipeline using CNNs to calibrate MKID arrays efficiently, significantly reducing tuning time while maintaining manual calibration accuracy.

Contribution

The paper presents a novel CNN-based pipeline for fully automating MKID resonator identification and tuning, replacing manual procedures with rapid, accurate machine learning methods.

Findings

Pipeline achieves manual tuning accuracy

Reduces calibration time from hours to minutes

Uses a single CNN for both 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 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.