Composite image of visible, microwave (orange) and X-ray (blue) data reveals the jets and radio-emitting lobes emanating from Centaurus A's central black hole. Image via ESO/WFI (visible); MPIfR/ESO/APEX/A.Weiss et al. (microwave); NASA/CXC/CfA/R.Kraft et al. (X-ray)
Using a new powerful algorithm developed at MIT, astronomers could soon crunch vast quantities of astronomical data to generate our first-ever image of a black hole.
While astronomy as a field of science continues to make new discoveries almost daily about the complexity of our universe, the black hole phenomenon remains one of the most perplexing, mostly because we’ve yet to actually photograph one.
Now, however, a team of computer scientists is coming to the aid of astronomy with a new algorithm that is capable of taking huge quantities of astronomical data and turning it into an image of a black hole.
CHIRP
According to MIT News, the algorithm is developed from data obtained from telescopes dotted across the world as part of the Event Horizon Telescope programme, which essentially aims to turn the Earth into one giant telescope.
Called Continuous High-resolution Image Reconstruction using Patch – or CHIRP for short – the algorithm developed by Katie Bouman and her team is aiming to help fill the gaps that would be left by such a collaborative telescope effort here on Earth.
Even with over a dozen telescopes signed up to Event Horizon Telescope – of which there are currently only six – gaps of thousands of kilometres would leave out significant amounts of data.
But by using a technique referred to as interferometry – which combines two telescope-detected signals to determine differences between them – CHIRP can find a way of neutralising challenges found with this method, such as signals slowing down as they enter the Earth’s atmosphere.
Example CHIRP reconstruction of a black hole image with the Event Horizon Telescope (EHT). Image via Jason Dexter, Monika Moscibrodzka, and Hotaka Shiokawa
‘Equivalent to taking an image of a grapefruit on the moon’
Using machine-learning, CHIRP is able to identify visual patterns that tend to recur in 64-pixel patches of real-world images and, during its development, was put through rigorous testing whereby it was presented with a large database of astronomical images and asked to look past the noise of the cosmos.
Thankfully, for Bouman and astronomy, it was found her algorithm could create an astrological image much better than previous algorithms, which could pave the way for our first image of a black hole.
Speaking of why CHIRP is necessary to create an image of a black hole, Bouman said: “[Taking a picture of the black hole in the centre of the Milky Way galaxy is] equivalent to taking an image of a grapefruit on the moon, but with a radio telescope.
“To image something this small means that we would need a telescope with a 10,000km diameter, which is not practical, because the diameter of the Earth is not even 13,000km.”
