We present an on-sky demonstration of a post-processing technique for companion detection called Stochastic Speckle Discrimination (SSD) and its ability to improve the detection of faint companions using SCExAO and the MKID Exoplanet Camera (MEC). Using this SSD technique, MEC is able to resolve companions at a comparable signal to noise to other integral field spectrographs solely utilizing photon arrival time information and without the use of any PSF subtraction techniques. SSD takes advantage of photon counting detectors, like the MKID detector found in MEC, to directly probe the photon arrival time statistics that describe the speckle field and allows us to identify and distinguish problematic speckles from companions of comparable brightness in an image. This technique is especially effective at close angular separations where the speckle intensity is large and where traditional post-processing techniques, like ADI, suffer.
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.
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