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Published 12:08 IST, April 11th 2019

This computer scientist developed algorithm to help astronomers produce first black hole image three years ago

Now we know for sure what the real, the actual black hole looks like in reality, thanks to scientists and astronomers. And from the image, it appears to as hot and beautiful as science fiction has made us believe over the years

Reported by: Tech Desk
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Now we know for sure what the real, the actual black hole looks like in reality, thanks to scientists and astronomers. And from the image, it appears to be as hot and beautiful as science fiction has made us believe over the years. But did you know that efforts to help produce the first ever image of a black hole were started in 2016?

READ | First ever black hole image: How it was captured and everything else you need to know

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"3 years ago MIT grad student Katie Bouman led the creation of an algorithm called CHIRP (Continuous High-resolution Image Reconstruction using Patch priors) to produce the first-ever image of a black hole."

READ | Anand Mahindra's comment on Black hole has a 'Dunkin Donut' & 'Sambar-Wada' connection & it's hilarious

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In 2016, an MIT graduate student in electrical engineering and computer science Katie Bouman led the development of the new algorithm that helped astronomers product the first image of a black hole.

Why and how the algorithm was developed?

The whole idea of the algorithm was to combine data collected from radio telescopes around the world with an aim to turn the entire planet into a large radio telescope dish. Katie said back then Radio wavelengths come with a lot of advantages and just like how radio frequencies will go through walls, they pierce through galactic dust.

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The problem with imaging a black hole which is "very far away and very compact" was astronomers would need a telescope with a 10,000-kilometer diameter, which was not practical

Bouman adopted a solution to multiply measurements from three radio telescopes for extra delays caused by atmospheric noise to cancel each other out. It meant that each new measurement required data from three telescopes, but the boost in terms of precision compensated for the loss of information.

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Bouman also used a machine-learning algorithm to recognise visual patterns to further improve her algorithm’s image reconstructions. For more details, you can read this article on MIT website.

12:08 IST, April 11th 2019