  The StarLight program conceptualizes fast interstellar travel via small wafer
satellites (wafersats) that are propelled by directed energy. This process is
wildly different from traditional space travel and trades large and slow
spacecraft for small, fast, inexpensive, and fragile ones. The main goal of
these wafer satellites is to gather useful images during their deep space
journey. We introduce and solve some of the main problems that accompany this
concept. First, we need an object detection system that can detect planets that
we have never seen before, some containing features that we may not even know
exist in the universe. Second, once we have images of exoplanets, we need a way
to take these images and rank them by importance. Equipment fails and data
rates are slow, thus we need a method to ensure that the most important images
to humankind are the ones that are prioritized for data transfer. Finally, the
energy on board is minimal and must be conserved and used sparingly. No
exoplanet images should be missed, but using energy erroneously would be
detrimental. We introduce simulator-based methods that leverage artificial
intelligence, mostly in the form of computer vision, in order to solve all
three of these issues. Our results confirm that simulators provide an extremely
rich training environment that surpasses that of real images, and can be used
to train models on features that have yet to be observed by humans. We also
show that the immersive and adaptable environment provided by the simulator,
combined with deep learning, lets us navigate and save energy in an otherwise
implausible way.
