This month marks the anniversary of a major achievement in astrophysics.
technique to reprocess the original data to reveal a new, sharper view of the blazing orange material swirling around M87*., which in turn will improve scientific analyses.
"With our new machine-learning technique, PRIMO, we were able to achieve the maximum resolution of the current array,"up close, the detail in an image plays a critical role in our ability to understand its behavior. 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."
The galaxy hosting M87*, Messier 87 , is located 55 million light-years away. It was chosen as the first target for the EHT because that's relatively close, and because, at 6.5 billion times the mass of the Sun, the supermassive black hole at its center is big and active enough that our current technology could resolve it.
Black holes, obviously, can't be seen themselves; but an active supermassive black hole, or one that is actively feeding on matter, has a hot disk andIt took seven radio telescopes around the globe combining their forces to create what is effectively one Earth-sized telescope, andto collect the data that eventually became the now-familiar image. Then came the data processing, and that was extremely labor-intensive.
However, while groundbreaking and effective, the technique of combining the seven telescopes – known as interferometry – is not perfect. There are gaps in the data, because the telescopes aren't actually one, big, Earth-sized receiver – they're physically spaced apart. So Medeiros and her colleagues developed a machine learning algorithm called principal-component interferometric modeling to fill in those gaps.
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