Science Breakthrough: Neural Network-Driven Approach Could Usher In A New Era Of Optical Astronomy, Researchers Say

AI powered by neural nets have not only excelled at tasks that require pattern recognition, they have also succeeded in performing tasks that require basic logic and reasoning -- areas that have conventionally been computers' Achilles' heel. In this backdrop, the question is, can neural networks be exploited to usher in an era of data-driven astronomy and create some of the sharpest ever images of celestial objects? The answer, according to a new study published in the Monthly Notices of the Royal Astronomical Society, is yes. In the paper, a group of researchers from the Swiss university ETH Zurich describe a neural net-enabled system that can recognize and reconstruct astronomical features that telescopes could not resolve, including star-forming regions, bars and streams of dust in galaxies. In order to create this system, the researchers used an adversarial learning technique, wherein two neural networks compete and try to outsmart each other.