Neural networks promise sharpest ever images

Telescopes, the workhorse instruments of astronomy, are limited by the size of the mirror or lens they use. Using ‘neural nets’, a form of artificial intelligence, a group of Swiss researchers now have a way to push past that limit, offering scientists the prospect of the sharpest ever images in optical astronomy. The new work appears in a paper in Monthly Notices of the Royal Astronomical Society.

The diameter of its lens or mirror, the so-called aperture, fundamentally limits any telescope. In simple terms, the bigger the mirror or lens, the more light it gathers, allowing astronomers to detect fainter objects, and to observe them more clearly. A statistical concept known as ‘Nyquist sampling theorem’ describes the resolution limit, and hence how much detail can be seen.

The Swiss study, led by Prof Kevin Schawinski of ETH Zurich, uses the latest in machine learning technology to challenge this limit. They teach a neural network, a computational approach that simulates the neurons in a brain, what galaxies look like, and then ask it to automatically recover a blurred image and turn it into a sharp one. Just like a human, the neural net needs examples – in this case a blurred and a sharp image of the same galaxy – to learn the technique. Source: Neural networks promise sharpest ever images

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