Featured Image Credit: EHT
Topics: Space
Gregory Robinson
Scientists have released a new version of the photo showing the first black hole ever seen, this time with ‘maximum resolution’.
The Event Horizon Telescope (EHT) collaboration gathered data on M87 - a black hole at the centre of a nearby galaxy - in 2017. The data from this was used to create an image of the ‘fuzzy, orange’ black hole.
The picture was released two years later however scientists have now given the picture a new and improved look with the help of artificial intelligence, giving us a full-resolution snap of that black hole for the first time.
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Scientists hope that the better image will provide more information about M87.
In a statement about the maximum resolution picture, lead author Lia Medeiros said: “With our new machine learning technique, PRIMO [principal-component interferometric modelling], we were able to achieve the maximum resolution of the current array. “Since we cannot study black holes up-close, the detail of an image plays a critical role in our ability to understand its behaviour.
“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.”
With PRIMO, which was developed by members of the EHT collaboration, researchers are able to piece together data from the seven existing EHT telescopes from around the world.
The EHT telescopes can be found in France, Spain, Greenland, Chile, Arizona and Hawaii in the US, Mexico and the South Pole. There are sometimes missing gaps when creating images because it’s unfeasible to put EHT telescopes everywhere on Earth.
The researchers published their findings regarding PRIMO and the new black hole image in a new paper, ‘‘The Image of the M87 Black Hole Reconstructed with PRIMO’, which was published in The Astrophysical Journal Letters.
“PRIMO is a new approach to the difficult task of constructing images from EHT observations,” said Tod Lauer, from NOIRLab, another member of the team.
“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.
“We are using physics to fill in regions of missing data in a way that has never been done before by using machine learning,” said Dr Medeiros. “This could have important implications for interferometry, which plays a role in fields from exo-planets to medicine.”