The image of the supermassive black hole at the heart of the galaxy Messier 87 was boosted to high fidelity by a machine learning program trained on black hole models.
This PRIMO refined image of M87* gives scientists a chance to better match observations of an actual black hole to theoretical predictions.
"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."Princeton's Institute of Advanced Study explained that PRIMO operates using dictionary learning, a branch of machine learning which enables computers to generate rules based on large sets of training material.
Once identified, these patterns were sorted based on how often they factored into simulations. This could then be incorporated into EHT images to create a high-fidelity image of M87* and reveal structures the telescope array may have missed.
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