  Machine learning, and eventually true artificial intelligence techniques, are
extremely important advancements in astrophysics and astronomy. We explore the
application of deep learning using neural networks in order to automate the
detection of astronomical bodies for future exploration missions, such as
missions to search for signatures or suitability of life. The ability to
acquire images, analyze them, and send back those that are important, as
determined by the deep learning algorithm, is critical in bandwidth-limited
applications. Our previous foundational work solidified the concept of using
simulator images and deep learning in order to detect planets. Optimization of
this process is of vital importance, as even a small loss in accuracy might be
the difference between capturing and completely missing a possibly-habitable
nearby planet. Through computer vision, deep learning, and simulators, we
introduce methods that optimize the detection of exoplanets. We show that
maximum achieved accuracy can hit above 98% for multiple model architectures,
even with a relatively small training set.
