This result provides overwhelming evidence that the object is indeed a black hole and yields valuable clues about the workings of such giants, which are thought to reside at the centre of most galaxies. First black hole image could change everything we know about the universe This week well see a direct image of one of the most powerful objects in existence, and it might shift our. In the future, this technique could help scientists get a better handle on the black hole’s mass and perform improved tests of gravity and other studies of black hole physics. Astronomers have unveiled the first image of the supermassive black hole at the center of our own Milky Way galaxy. But the new technique uses machine learning to fill in those gaps based on over 30,000 simulated images of matter swirling around a black hole, creating a sharper image. On Wednesday, April 10th, the world was treated to something unprecedented - the first-ever image of a black hole Specifically, the image captured the Supermassive Black Hole (SMBH). Previous analyses had used certain assumptions to fill in those gaps, such as preferring an image that is smooth. “We need to have an algorithm that can fill in those gaps.” “Since we can’t just cover the entire Earth in telescopes, what that means is that there is some missing information,” says astrophysicist Lia Medeiros of the Institute for Advanced Study in Princeton, N.J. But that technique leaves holes in the data. The Event Horizon Telescope takes data using a network of telescopes across the globe. This video morphs from the fuzzier image into its new, improved version. A machine learning technique allowed scientists to refine that image to reveal a thinner band. The supermassive black hole in the galaxy M87, imaged by the Event Horizon Telescope in 2019, originally looked like a fuzzy ring, created by the glowing gas surrounding the black hole. (image credit #EHTblackhole #BlackHoleDay #BlackHole created a new, sharper version of the first image of a black hole. Right: MIT computer scientist Margaret Hamilton w/the code she wrote that helped put a man on the moon. In 2019, the international Event Horizon Telescope (EHT) collaboration introduced the first-ever images of a black hole, called M87, located at the center. It measures 40 billion km across - three million times the size of the Earth - and has been. Left: MIT computer scientist Katie Bouman w/stacks of hard drives of black hole image data. The first time astronomers tried this technique with high enough sensitivity to measure a black hole, in 2006, the team failed miserably, according to Shep Doeleman, then an astronomer at. Astronomers have taken the first ever image of a black hole, which is located in a distant galaxy. So much data was collected by Event Horizon Telescope that it had to be shipped to the MIT Haystack Observatory on half a ton of hard drives. CHIRP can also be used for any imaging system that uses radio interferometry. The algorithm then reconstructed and refined the original images to prepare the final historical image of the black hole. This does mean that each new measurement requires data from three telescopes, not just two, but the increase in precision makes up for the loss of information. Since astronomical signals reach the radio telescopes at slightly different rates, the researchers had to figure out how to account for that so calculations would be accurate and visual information could be extracted.īouman adopted a clever algebraic solution to this problem: If the measurements from three telescopes are multiplied, the extra delays caused by atmospheric noise cancel each other out. As MIT described it three years ago, the project sought “to turn the entire planet into a large radio telescope dish.” The development of CHIRP was announced in 2016 by MIT and involved a team of researchers from three places: MIT’s Computer Science and Artificial Intelligence Laboratory, the Harvard-Smithsonian Center for Astrophysics and the MIT Haystack Observatory. The algorithm, which Bouman named CHIRP (Continuous High-resolution Image Reconstruction using Patch priors) was needed to combine data from the eight radio telescopes around the world working under Event Horizon Telescope, the international collaboration that captured the black hole image, and turn it into a cohesive image.ģ years ago MIT grad student Katie Bouman led the creation of a new algorithm to produce the first-ever image of a black hole.Ģ016 story: #EHTblackhole #EventHorizonTelescope /u6FBswmGDZīouman is currently a postdoctoral fellow with Event Horizon Telescope and will start as an assistant professor in Caltech’s computing and mathematical sciences department, according to her website. Bouman shared a photo on Facebook of herself reacting as the historical picture was processing. The development of the algorithm that made it possible to create the first image ever of a black hole was led by computer scientist Katie Bouman while she was still a graduate student at MIT.
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