What if a computer programme was able to take an image captured by a telescope and calculate how the target object would look if seen in greater detail? A group of Swiss scientists are hoping their new programme can do just that.
Using a system called ‘neural nets’, which enables computers to learn in the same way as a human brain, the team began by showing the computer a blurred and a sharpened image of the same galaxy.
It was then able to use this knowledge to clarify a blurred galaxy image by itself.
Not only this, the computer was also able to recognise and resolve features that the telescope could not, like dust lanes, bars and star-forming regions.
This technique, once refined, could enable astronomers to see farther into space than the light-gathering capabilities of the biggest telescopes allow.
“We can start by going back to sky surveys made with telescopes over many years, see more detail than ever before, and for example learn more about the structure of galaxies,” says Professor Kevin Schawinski of ETH Zürich, who led the study.
“There is no reason why we can’t then apply this technique to the deepest images from Hubble, and the coming James Webb Space Telescope, to learn more about the earliest structures in the Universe.”