In 2019, the first image of a black hole was published. Specifically, the supermassive black hole at the center of the galaxy M87, whose appearance was reminiscent of an orange donut. This Thursday, an article published in the journal ‘The Astrophysical Journal Letters’ includes an updated version of the iconic photograph, which provides a more accurate representation of the astronomical object.
The original image was captured using the Event Horizon Telescope (EHT), which uses a network of ‘connected’ international telescopes that operate as a single instrument. As a result, a resolution equivalent to that of an Earth-sized telescope, otherwise impossible to build, is created.
The famous capture showed the black hole as a fuzzy structure reminiscent of an orange donut, and confirmed the existence of the event horizon, the region of space surrounding a black hole where gravity is so strong that nothing can escape, not even the light.
However, photography had some limitations, due to the technique used. The telescopes in the EHT network only collected information over a short window of time and from different locations on Earth, resulting in ‘gaps’ in the data. Furthermore, the results were incomplete and contaminated with noise (unwanted disturbances that affect the measurement data).
Now, with the help of a new machine learning technique, called PRIMO, scientists have been able to reconstruct a more detailed and complete picture. This photograph shows a larger, darker central region, indicating the black hole is larger than originally thought, and the orange circumference surrounding it is thinner and brighter.
“PRIMO provides a way to compensate for missing information about the object being observed and generates the image that would have been seen using a single gigantic Earth-sized radio telescope (which is impossible to build), rather than a network of EHT telescopes,” says Tod Lauer, co-author of the study and astronomer at NOIRLab (National Optical and Infrared Astronomy Research Laboratory).
To ‘complete’ the black hole picture, scientists analyzed with PRIMO more than 30,000 simulated images of black holes accreting matter through gravity, a process called gas accretion, and looked for common patterns in the structure of the images. . Finally, a high-fidelity estimate of the missing structure was provided from the original images taken by EHT. “We are using physics to fill in missing regions of data in a way that has never been done before using machine learning,” said Lia Medeiros, co-author of the study and an astrophysicist at the Institute for Advanced Studies (USA).
The team confirmed that the new image is consistent with the EHT data and theoretical expectations, including the bright ring of emission expected to be produced by hot gas falling into the black hole. “Approximately four years after the first horizon-scale image of a black hole was revealed by EHT, we have marked another milestone,” celebrated Dimitrios Psaltis, co-author of the study and astronomer at the Georgia Institute of Technology (USA). “The new machine learning techniques we have developed provide a golden opportunity for our collective work to better understand the physics of black holes.”
The new image will allow more precise determinations of the mass of the M87 black hole and the physical parameters that determine its current appearance, as well as more robust gravity tests, among others. PRIMO can also be applied to additional EHT observations, including those of Sgr A*, the central black hole of our own Milky Way galaxy, and “could have important implications for interferometry applied to different fields,” adds Medeiros.
Interferometry is a technique used in astronomy to combine information collected by two or more separate telescopes, in order to obtain more detailed images of celestial objects. It can also be applied in medicine, for example, for cancer detection. “The 2019 image was just the beginning,” says the scientist. «If a picture is worth a thousand words, the data underlying that image has many more stories to tell. PRIMO will continue to be a critical tool for extracting such insights.”