Direct Exoplanet Detection Using Deep Convolutional Image Reconstruction
  (ConStruct): A New Algorithm for Post-Processing High-Contrast Images
Trevor N. Wolf Brandon A. Jones Brendan P. Bowler
We present a novel machine-learning approach for detecting faint point
sources in high-contrast adaptive optics imaging datasets. The most widely used
algorithms for primary subtraction aim to decouple bright stellar speckle noise
from planetary signatures by subtracting an approximation of the temporally
evolving stellar noise from each frame in an imaging sequence. Our approach
aims to improve the stellar noise approximation and increase the planet
detection sensitivity by leveraging deep learning in a novel direct imaging
post-processing algorithm. We show that a convolutional autoencoder neural
network, trained on an extensive reference library of real imaging sequences,
accurately reconstructs the stellar speckle noise at the location of a
potential planet signal. This tool is used in a post-processing algorithm we
call Direct Exoplanet Detection with Convolutional Image Reconstruction, or
ConStruct. The reliability and sensitivity of ConStruct are assessed using real
Keck/NIRC2 angular differential imaging datasets. Of the 30 unique point
sources we examine, ConStruct yields a higher S/N than traditional PCA-based
processing for 67$\%$ of the cases and improves the relative contrast by up to
a factor of 2.6. This work demonstrates the value and potential of deep
learning to take advantage of a diverse reference library of point spread
function realizations to improve direct imaging post-processing. ConStruct and
its future improvements may be particularly useful as tools for post-processing
high-contrast images from the James Webb Space Telescope and extreme adaptive
optics instruments, both for the current generation and those being designed
for the upcoming 30 meter-class telescopes.