The iconic Event Horizon Telescope (EHT) image of the supermassive black hole at the center of Messier 87 has received its first official makeover, thanks to the Principal-component Interferometric Modeling (PRIMO), a new machine-learning technique that uses dictionary learning to correct for the sparse Fourier-domain coverage of the EHT interferometric visibilities.
Messier 87 is a giant elliptical galaxy located some 53 million light-years away in the constellation of Virgo.
In April 2019, the EHT Collaboration released stunning images of M87*, the supermassive black hole in the center of Messier 87.
Those images were produced using EHT observations performed in April 2017.
To collect the data, the EHT astronomers used a network of seven radio telescopes at different locations around the world to form an Earth-sized virtual telescope with the power and resolution capable of observing the ‘shadow’ of a black hole’s event horizon.
Though this technique allowed the team to see remarkably fine details, it lacked the collecting power of an actual Earth-sized telescope, leaving gaps in the data.
Described in a paper in the Astrophysical Journal, the new machine-learning technique helped fill in those gaps.
“With PRIMO we were able to achieve the maximum resolution of the current array,” said Dr. Lia Medeiros, an astronomer with Steward Observatory at the University of Arizona and the Institute for Advanced Study.
“Since we cannot study black holes 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.”
PRIMO relies on a branch of machine learning known as dictionary learning, which teaches computers certain rules by exposing them to thousands of examples.
The power of this type of machine learning has been demonstrated in numerous ways, from…
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