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Humans May See Real Black Holes Soon

First Posted: Jun 08, 2016 06:10 AM EDT
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Humans may eventually see a black hole with their own eyes. If a new computer algorithm, formulated by MIT graduate student Katie Bouman and her team, works that is.

Black holes are very small, so it would take a really large telescope to be able to see them. The "supermassive" black hole at the center of Milky Way galaxy may be 17 times the diameter of the sun but it is 25,000 light years away. To be able to see it, a gigantic telescope is necessary, as reported by BBC

This is what driven Bouman and her team to do their research. Bouman, speaking with Popular Science, shared that a telescope the size of the Earth is probably just right to get a picture of what the black hole looks like. Obviously, that is next to impossible to create.

What scientists currently use in order to study black holes is a group of telescopes run by the Event Horizon Telescope project. The different telescopes careered all over the planet will get data simultaneously and these data will be collated by the scientists to do their analysis.

Needless to say, sorting the data to come up with an accurate analysis is not the easiest thing to do. Not to mention, the method does not churn out clear data. Usually,  the data received are messy and muddled because they are received at different times. 

Bouman and her team will not let the impossibility of building an Earth-size telescope stop them from getting a better view of the black holes, though. This is why they came up with the CHIRP (Continuous High-resolution Image Reconstruction using Patch priors) algorithm, designed to improve the system of gathering black hole data and achieve the goal of better black hole view. 

With CHIRP, a lot of images from space will use images from space as references to form a mosaic that would best match the data from the telescopes. Compared to the current system of getting data, CHIRP has a better capacity at picking out what can be considered important data and what is not, so it can turn practice data into sharp pictures much more effectively.

Seeing that Bouman has a computing and not physics background, what she and her team made can be considered remarkable. "In the end, I'd like to excite people in the computational imaging and computer vision communities to work on this problem, too," she said.

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