Machine learning reconstructs new image from EHT data. The image of the M87 black hole has been enhanced using a machine learning technique called PRIMO, providing a more accurate representation and allowing for improved determinations of its mass and physical parameters. The iconic image of the
New image of M87 supermassive black hole generated by the PRIMO algorithm using 2017 EHT data. Credit: Medeiros et al. 2023has been enhanced using atechnique called PRIMO, providing a more accurate representation and allowing for improved determinations of its mass and physical parameters.of the supermassive black hole at the center of M87—sometimes referred to as the “fuzzy, orange donut”—has gotten its first official makeover with the help of machine learning.
“With our new machine learning technique, PRIMO, we were able to achieve the maximum resolution of the current array,” says lead author Lia Medeiros of the Institute for Advanced Study. “Since we cannot studyup close, the detail of 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 dictionary learning, a branch of machine learning which enables computers to generate rules based on large sets of training material. For example, if a computer is fed a series of different banana images—with sufficient training—it may be able to determine if an unknown image is or is not a banana. Beyond this simple case, the versatility of machine learning has been demonstrated in numerous ways: from creating Renaissance-style works of art to completing the.
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