![]() ![]() This causes the gas to heat up and glow as it whips around the light-trapping surface that forms the outer bounds of the black hole called the event horizon. These models explain that the bright ring seen in these images of M87* is the result of gas being accelerated to near-light speeds by the incredible gravitational influence of the black hole. The resultant image rendered by PRIMO agrees with EHT data and theoretical black hole models. ![]() "This could have important implications for interferometry, which plays a role in fields from exoplanets to medicine." "We are using physics to fill in regions of missing data in a way that has never been done before by using machine learning," Medeiros explained. This could then be incorporated into EHT images to create a high-fidelity image of M87* and reveal structures the telescope array may have missed. Once identified, these patterns were sorted based on how often they factored into simulations. To train PRIMO to do the same thing with black holes the team fed it 30,000 high-fidelity simulated images of these cosmic titans as they feed on surrounding gas, a process called "accretion." The images covered a wide spread of theoretical predictions of how black holes accrete matter allowing PRIMO to hunt for patterns. So for example, if a program like this is fed a number of images of a banana it can learn to determine if an image of an unknown object is a banana or not. The Institute for Advanced Study in Princeton, New Jersey explained that PRIMO operates using dictionary learning, a branch of machine learning which enables computers to generate rules based on large sets of training material. "It provides a way to compensate for the missing information about the object being observed, which is required to generate the image that would have been seen using a single gigantic radio telescope the size of the Earth." Training PRIMO to build a better black hole "PRIMO is a new approach to the difficult task of constructing images from EHT observations," EHT member and NOIRLab researcher Tod Lauer said in the statement. This PRIMO refined image of M87* gives scientists a chance to better match observations of an actual black hole to theoretical predictions. When the image of the M87 supermassive black hole (M87*), which is 55 million light-years from Earth and has a mass equivalent to six and a half billion suns, was first revealed, scientists were astounded about just how well it matched predictions made by Albert Einstein's 1915 general theory of relativity. "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," research lead author Medeiros said in a statement. The EHT is a network of seven telescopes across the globe that creates an Earth-sized telescope, but despite its combined observing power, there are still gaps in the data it collects, much like the missing pieces of a jigsaw puzzle.Ī team of researchers including EHT collaboration member and astrophysics postdoctoral fellow Lia Medeiros, used a new machine learning technique called principal-component interferometric modeling or "PRIMO" to "fill in the gaps" in the M87 image and boost the EHT array to its maximum resolution for the first time. Medeiros (Institute for Advanced Study), D. At right is a new image of the black hole generated by the PRIMO algorithm using the same data set. At left is the famous image of the M87 supermassive black hole originally published by the Event Horizon Telescope collaboration in 2019. ![]()
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