In 2019, the Event Horizon Telescope Collaboration’s historic image presented the first direct visual evidence that black holes exist. It showcased a “fuzzy, orange donut” with a central dark region encapsulated by a prong of light, but now the image is “sharper”. Scientists used a machine learning technique, PRIMO, to make the image look more like a “skinny” doughnut. In addition, they were able to achieve maximum resolution using photo archive data and PRIMO neural networks. The new technique for constructing images from EHT helps compensate for the missing information about the object being observed – in this case, the supermassive black hole at the center of the galaxy Messier 87, or M87, 55 million light-years from Earth.
The upgraded image allows scientists to make more accurate measurements of the black hole’s mass and means that the width of the ring is now smaller. This new detail can provide a powerful constraint for theoretical models and tests of gravity. By analyzing more than 30,000 high-resolution simulated images of black holes to pick out common structural details, machine learning can now fill in gaps in the original image. This is essential since, as we cannot study black holes up-close, the detail of an image plays a critical role in our ability to understand its behavior.
The visual confirmation of black holes acts as confirmation of Albert Einstein’s theory of general relativity. If heated materials in the form of plasma surround the black hole and emit light, the event horizon can be visible. In the future, PRIMO will continue to be a critical tool in extracting insights from black hole data. A word of caution though – the image needs to be interpreted within the limitations of what we can see from 55 million light-years away, but this scientific breakthrough is a testament to how technology can help us unlock the secrets of the universe.
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