Four years ago, the first image of a crow black hole proved that the web can be attracted to something more than just a bunch of celebrity temptations. Last year, that excitement was continued in images of another huge and unimaginably massive black hole in our own galaxy. Work is continuing, and this year the raw data has been processed by AI to make the image clearer. More examples are on the horizon, so maybe it's a good time to think about how to get these done.
The first thing to note is that none of these images are actually images of a black hole. The defining feature of a black hole is that the gravitational pull is so strong that not even light can escape. So, no matter what instrument is used, we don't actually see them. However, supermassive black holes, in particular, are often surrounded by an accretion disk that radiates outside the event horizon, which can be so bright that the black hole itself stands out if the orientation is right.
Despite the brightness of these accretion disks, the accretion disks of supermassive black holes are not easy to observe. The same goes for some of the reasons why these images require one of the greatest collaborations in astronomical history.
On the one hand, M87* (the star that distinguishes supermassive black holes from its galaxies) is far away. To be exact, 54 million light-years away. Although the accretion disk is already large for our solar system standards, occupying several light days, it is still difficult to resolve at such a distance. Sagittarius A* is 2,000 times closer to us, but there is a lot of dust and other stars blocking our view.
In order to give distant objects some resolution, it is best to have a huge telescope - such as a telescope the size of the Earth. Even if no one mistook it for the Death Star and bombed its garbage passages, the price would be quite expensive.
However, astronomers have eight radio telescopes scattered across Earth collaborating on a project called the Event Horizon Telescope (EHT). The spacing of these telescopes gives us a high-resolution baseline, just as the distance between your two eyes gives you an extra sense of depth.
Your brain has evolved over millions of years to merge images from two eyes. Telescopes achieve the same effect with interferometry, which relies on how the crests and troughs of electromagnetic waves affect each other, resulting in intensity patterns based on the phase differences of the waves. In this way, astronomers use radio telescopes that can produce details much higher than the capacity of each individual telescope.
Today, interferometric techniques with very long baselines allow us to connect distant telescopes into a single whole. Merging this data into a picture requires enormous computing power, but as the technology becomes more common, astronomers can perform this feat from a wider range of locations.
In EHT's case, eight telescopes are located in Hawaii, California, Arizona, Mexico, Chile, Greenland, Spain, and France. Radio telescopes are not as susceptible to clouds as optical instruments, but storms and even high winds can interfere. Due to the simultaneous need for observations, the project had to wait for all locations to calm down at the same time.
Since the transmission of data between telescopes exceeds the capacity of the intercontinental transmission network, the data is stored on a set of hard disks that must be collected together. Each observation is timed by an atomic clock in nanoseconds. When combining data, it is necessary to calculate the time it takes for different instruments to receive radio waves at the speed of light.
Even with all these observational capabilities, astronomers cannot simply merge radio waves collected by telescopes and then convert them into images that our eyes can see. The original product is too unclear for this.
Interference from our atmosphere to the periphery of the center of the M87* galaxy must be identified and removed. This process becomes more difficult when further observations of Sagittarius A* are repeated because there is more media interference.
Finally, the EHT team compared the observations with decades of computer models of how black holes distort the space around them and the expected behavior of accretion disk matter. This relies on research results that we know, or think we know, about the behavior of this high-temperature substance under strong gravitational and magnetic conditions.
This level of uncertainty is why AI was able to make the same picture clearer after learning Xi 30,000 simulated images of the event horizon.
The same interferometric technique allowed astronomers to re-align the same telescope at M87*, revealing the jet produced when the supermassive black hole devoured the star.