This is the sharpest image yet of a Black Hole.
Machine-Learning Method Sharpens Messier 87 Black Hole Imaging
A New Look at the First Black Hole Image
Researchers, including an astronomer from NSF’s NOIRLab, have developed a machine-learning technique called PRIMO to improve the clarity and precision of radio interferometry images. This new approach has been utilized to create a high-fidelity version of the famous Event Horizon Telescope (EHT) image of the supermassive black hole at the heart of Messier 87.
A Makeover for the Iconic Black Hole Image
The iconic image of the supermassive black hole in Messier 87 has undergone its first official update, thanks to the PRIMO machine-learning technique. This enhanced image more accurately depicts the black hole’s dark core and the unexpectedly narrow outer ring. The researchers generated the new image using the original 2017 data from the EHT collaboration and, for the first time, achieved the full resolution of the EHT.
Developing PRIMO: A Machine-Learning Breakthrough
EHT members Lia Medeiros (Institute for Advanced Study), Dimitrios Psaltis (Georgia Tech), Tod Lauer (NSF’s NOIRLab), and Feryal Ozel (Georgia Tech) developed PRIMO, which stands for principal-component interferometric modeling. Their work is detailed in The Astrophysical Journal Letters.
Filling the Gaps in Radio Telescope Data
In 2017, the EHT collaboration utilized a global network of seven radio telescopes to form an Earth-sized virtual telescope capable of observing the “shadow” of a black hole’s event horizon. While this technique enabled astronomers to capture incredibly detailed images, it lacked the data-collecting power of a real Earth-sized telescope, leaving gaps in the information. The new PRIMO technique addresses these gaps.
Achieving Maximum Resolution with PRIMO
Lia Medeiros, the study’s lead author, explains that PRIMO helped the team reach the maximum resolution of the current array. The narrower ring width in the image will provide powerful constraints for theoretical models and gravity tests. PRIMO uses a branch of machine learning called dictionary learning, which trains computers on specific rules using thousands of examples.
Applying PRIMO to EHT Data
When PRIMO was applied to the EHT image of Messier 87, computers examined over 30,000 high-fidelity simulated images of gas accreting onto a black hole, searching for common patterns. These results were then combined to create a highly accurate representation of the EHT observations, while also providing a high-fidelity estimate of the missing image structure. A paper on the PRIMO algorithm was published in The Astrophysical Journal on February 3, 2023.
PRIMO’s Potential for Future Observations
The updated image is consistent with both EHT data and theoretical expectations, including the bright emission ring produced by hot gas falling into the black hole. This new image should lead to more precise determinations of Messier 87’s black hole mass and the physical parameters shaping its appearance. PRIMO can also be applied to other EHT observations, such as those of Sagittarius A*, the central black hole in the Milky Way.
Continuing to Unlock the Secrets of Black Holes
Lia Medeiros affirms that the 2019 image was just the beginning and that PRIMO will be a crucial tool in extracting further insights from the data underlying the image.
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