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MIT Develops New Machine-Learning Algorithm Designed to Capture True Images of A Black Hole

Jun 09, 2016 01:50 AM EDT

A team of researchers led by MIT's Katie Bouman have developed a new computer algorithm that could help astronomers generate the first true image of a black hole.

At present, astronomers rely of imaginative minds of artists to create a clear image of a black hole. Black holes are very compact and are very far away from Earth, making it harder for astronomers to create a high-quality photo.

"Taking a picture of the black hole in the center of the Milky Way galaxy is equivalent to taking an image of a grapefruit on the moon, but with a radio telescope," Bouman explained in a press release.

For years, researchers have been using radio wavelengths to detect and explore distant objects due to the ability of radio frequencies to penetrate through galactic dust. However, due to their long wavelengths, radio waves requires long antenna.

Currently, the largest single radio-telescope dish in the world has a diameter of 1,000 feet. But even with that size, the images it produced of the moon could appear blurrier than the image seen in an ordinary backyard optical telescope.

In order to capture a high-definition image of black hole, scientists need to build a very large telescope about 10,000 kilometers in diameter, which is very absurd because the Earth only has a diameter of about 13,000 kilometers.

To overcome this impracticality, Bourman developed a machine-learning algorithm that can stitch together data collected from different radio telescopes around the world that are participating the Event Horizon Telescope project. The project, which is currently joined by six observatories, aims to turn the entire planet into a huge radio telescope dish.

According to the report from Washington Post, the new algorithm, dubbed as Continuous High-resolution Image Reconstruction or CHIRP, has the ability to sort out useful information and discard irrelevant data. It can then create mosaic-like images using matching data from different telescope, resulting to a sharper picture.

Bouman and her team are set to present their new algorithm at the Computer Vision and Pattern Recognition conference this month.

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