Researchers used machine learning to improve the first photo of a black hole | Engadget

The original images were captured in 2017 by the Event Horizon Telescope , a network of radio telescopes around Earth that combine to act as a planet-sized super-imaging tool. The initial picture looked like a “fuzzy donut,” as, but researchers used a new method called PRIMO to reconstruct a more accurate image.

Black holes are mysterious and strange regions of space where gravity is so strong that nothing can escape. They form when dying stars collapse onto themselves under their gravity. As a result, the collapse squeezes the star’s mass into a tiny space. The boundary between the black hole and its surrounding mass is called the event horizon, a point of no return where anything that crosses it won’t be coming back.

“What we really do is we learn the correlations between different parts of the image. And so we do this by analyzing tens of thousands of high-resolution images that are created from simulations,” the astrophysicist and author of the paper Lia Medeiros of the Institute for Advanced Study in Princeton, NJ, told. “If you have an image, the pixels close to any given pixel are not completely uncorrelated. It’s not that each pixel is doing completely independent things.

Errrr

OMG! The difference is so magnificent! I kneel before my AI master. Or it’s slightly more in focus but not really. Oh and it’s based off what we think a black hold looks like not what it really looks like. LOL.

Oh great, now it kinda looks like Uranus...

It’s trained in generated images of what we think a blackhole should look like. Given this is the first black hole we’ve ever imaged, real life accuracy seems important. This is a little like Samsung’s space zoom.

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