Revolutionizing Black Hole Imaging: Unveiling the Power of PRIMO and the Event Horizon Telescope Collaboration

 In a groundbreaking study published in The Astrophysical Journal Letters, researchers presented an extraordinarily detailed image of the M87 black hole, captured using data collected by the Event Horizon Telescope (EHT) collaboration in 2017. This black hole, located at the heart of the massive elliptical galaxy Messier 87, is situated 55 million light-years away from Earth.

Left, the image of the supermassive black hole M87 originally published by the EHT collaboration in 2019; right, the new image generated by the PRIMO algorithm using the same data. Credit: EHT.

Back in 2017, the EHT collaboration utilized a global network of seven radio telescopes to gather information on M87, effectively creating an "Earth-sized telescope." However, due to the impracticality of covering the entire planet with telescopes, data gaps were inevitable.

To address this issue, EHT members Lia Medeiros, Dimitrios Psaltis, Tod Lauer, and Feryal Özel developed a novel machine learning technique called Principal-component Interferometric Modeling (PRIMO). PRIMO employs dictionary learning, a method that enables computers to establish pattern recognition rules based on extensive datasets.

Over 30,000 simulated black hole images, including accretion disks comprised primarily of orbiting gas and dust, were analyzed. The simulations covered a broad range of models, with those most closely resembling real data being selected. These models were then ranked according to their frequency in the simulations and combined to create the most accurate and precise depiction of the observations, overcoming the aforementioned data limitations.

The enhanced M87 image has improved estimations of various parameters, such as its mass. PRIMO could also be applied to future EHT observations, like those of Sgr A* – the black hole at the center of our own galaxy, the Milky Way.

ApJ, IAS.