![]() This allowed the machine learning essentially to fill in the gaps of the original image. If a computer is given a series of images of different bananas, combined with some training, it might be able to tell if an unknown image does or doesn’t contain a banana.Īstronomers discover ultramassive black hole using new techniqueĬomputers using PRIMO analyzed more than 30,000 high-resolution simulated images of black holes to pick out common structural details. The algorithm relies on dictionary learning in which computers create rules based on large amounts of material. Medeiros and other EHT members developed Principal-component Interferometric Modeling, or PRIMO. ![]() The width of the ring in the image is now smaller by about a factor of two, which will be a powerful constraint for our theoretical models and tests of gravity.” “Since we cannot study black holes up-close, the detail of an image plays a critical role in our ability to understand its behavior. “With our new machine learning technique, PRIMO, we were able to achieve the maximum resolution of the current array,” said lead study author Lia Medeiros, astrophysics postdoctoral fellow in the School of Natural Sciences at the Institute for Advanced Study in Princeton, New Jersey, in a statement. NASA, ESA, Zachary Schutte (XGI), Amy Reines (XGI), Alyssa Pagan (STScI)Ī black hole fueling star birth has scientists doing a double-take The bright region at the center, surrounded by pink clouds and dark dust lanes, indicates the location of the galaxy's massive black hole and active stellar nurseries. The new, more detailed image, along with a study, was released on Thursday in The Astrophysical Journal Letters.ĭwarf starburst galaxy Henize 2-10 sparkles with young stars in this Hubble visible-light image. This array effectively created a virtual telescope around the same size as Earth.ĭata from the original 2017 observation was combined with a machine learning technique to capture the full resolution of what the telescopes saw for the first time. To capture an image of the black hole, scientists combined the power of seven radio telescopes around the world using Very-Long-Baseline-Interferometry, according to the European Southern Observatory, which is part of the EHT. The project was named for the event horizon, the proposed boundary around a black hole that represents the point of no return where no light or radiation can escape. More than 200 researchers worked on the project for more than a decade. The Event Horizon Telescope Collaboration, called EHT, is a global network of telescopes that captured the first photograph of a black hole. In 2017, astronomers set out to observe the invisible heart of the massive galaxy Messier 87, or M87, near the Virgo galaxy cluster 55 million light-years from Earth. A machine-learning technique was used to enhance the Event Horizon Telescope Collaboration's image (left) of the supermassive black hole at the center of the galaxy Messier 87 and produce a sharper image.
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