The iconic Event Horizon Telescope (EHT) image of the supermassive black hole at the center of Messier 87 has received its first official makeover, thanks to the Principal-component Interferometric Modeling (PRIMO), a new machine-learning technique that uses dictionary learning to correct for the sparse Fourier-domain coverage of the EHT interferometric visibilities.
New image of M87* generated by the PRIMO algorithm using the 2017 EHT data. Image credit: Medeiros et al., doi: 10.3847/2041-8213/acc32d.
Messier 87 is a giant elliptical galaxy located some 53 million light-years away in the constellation of Virgo.
In April 2019, the EHT Collaboration released stunning images of M87*, the supermassive black hole in the center of Messier 87.
Those images were produced using EHT observations performed in April 2017.
To collect the data, the EHT astronomers used a network of seven radio telescopes at different locations around the world to form an Earth-sized virtual telescope with the power and resolution capable of observing the ‘shadow’ of a black hole’s event horizon.
Though this technique allowed the team to see remarkably fine details, it lacked the collecting power of an actual Earth-sized telescope, leaving gaps in the data.
Described in a paper in the Astrophysical Journal, the new machine-learning technique helped fill in those gaps.
“With PRIMO we were able to achieve the maximum resolution of the current array,” said Dr. Lia Medeiros, an astronomer with Steward Observatory at the University of Arizona and the Institute for Advanced Study.
“Since we cannot study black holes up close, the detail in 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.”
The original EHT image of M87*. Image credit: EHT Collaboration.
PRIMO relies on a branch of machine learning known as dictionary learning, which teaches computers certain rules by exposing them to thousands of examples.
The power of this type of machine learning has been demonstrated in numerous ways, from creating Renaissance-style works of art to completing the unfinished work of Beethoven.
Applying PRIMO to the EHT image of M87*, computers analyzed over 30,000 high-fidelity simulated images of gas accreting onto a black hole to look for common patterns in the images.
The results were then blended to provide a highly accurate representation of the EHT observations, simultaneously providing a high-fidelity estimate of the missing structure of the image.
“PRIMO is a new approach to the difficult task of constructing images from EHT observations,” Dr. Lauer said.
“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.”
The authors confirmed that the newly rendered image of M87* is consistent with the EHT data and with theoretical expectations, including the bright ring of emission expected to be produced by hot gas falling into the black hole.
The new image should lead to more accurate determinations of the black hole’s mass and the physical parameters that determine its present appearance.
PRIMO can also be applied to additional EHT observations, including those of Sagittarius A*, the central black hole in our own Milky Way Galaxy.
“The 2019 image was just the beginning,” Dr. Medeiros said.
“If a picture is worth a thousand words, the data underlying that image have many more stories to tell.”
“PRIMO will continue to be a critical tool in extracting such insights.’
The team’s paper was published in the Astrophysical Journal Letters.
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Lia Medeiros et al. 2023. The Image of the M87 Black Hole Reconstructed with PRIMO. ApJL 947, L7; doi: 10.3847/2041-8213/acc32d
Lia Medeiros et al. 2023. Principal-component Interferometric Modeling (PRIMO), an Algorithm for EHT Data. I. Reconstructing Images from Simulated EHT Observations. ApJ 943, 144; doi: 10.3847/1538-4357/acaa9a
