“That gives us a lot of trust that the image that they’re deriving is, if not the best, then one of the best that can be predicted from the data,” she says. This consistency is “very good,” says Jessica Lu, an astronomy professor at the University of California, Berkeley, who was not involved in the research. The researchers found the resulting image to be a good fit with theoretical expectations. The result was an image with a much thinner orange ring than seen in the original image and with a brighter rim at the bottom. Once the machine-learning algorithm had been trained with these images, the team used it to build an image of the black hole from the M87-data collected by EHT. The team’s suite of 30,000 synthetic images were generated from simulated black holes that had different masses, as well as different environments of accreting matter. To prevent their machine-learning algorithm from creating an image of what M87 was expected to look like, rather than of what the black hole actually looks like, the researchers included a broad range of black holes in their training procedures. “The most important thing with any training algorithm or any machine-learning algorithm is to ensure that your training set does not have preconceived ideas of what the result is going to be,” says Psaltis. Here the researchers produced a set of simulated black holes and then determined how those black holes would appear in EHT observations, creating a large suite of synthetic black hole images with which to train the algorithm. For instance, an algorithm of this kind, after being trained with a broad variety of images of different types of dogs, could learn to recognize and analyze the image of a dog, says Dimitrios Psaltis, coauthor of the paper. The approach is used in image recognition. The new image is obtained with a machine-learning approach called dictionary learning, which uses a large set of training material to extract rules for analyzing data. Their new high-fidelity reconstruction of M87-when compared to the 2019 image-reveals a better defined central region surrounded by a thinner bright ring of accreting gas. Now a team of researchers shows the power of machine learning in performing this task. However, since the telescopes can’t cover the whole planet, the image has to be constructed from incomplete snapshots from each telescope. In 2019, the Event Horizon Telescope (EHT) Collaboration unveiled the first-ever image of a black hole, which some described as a “fuzzy, orange donut.” EHT involves a global array of radio telescopes, which together create an effective Earth-sized observatory with high resolution. (Bottom) Improved image obtained by Medeiros et al. (Top) First image of the black hole published in 2019.
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