Scientists believe the environment immediately surrounding the black hole is tumultuous. It features hot, magnetised gas that spirals in a disk at tremendous speeds and temperatures. Astronomical observations remark that mysterious flares occur several times a day within such a disk, temporarily brightening and then fading away. 

A team from Caltech has used telescope data and an artificial intelligence (AI) computer vision technique to recover the first three-dimensional video showing what such flares could look like around Sagittarius A* (Sgr A*, pronounced sadge-ay-star), the supermassive black hole at the heart of our Milky Way galaxy.

These 3D flare structures feature two bright, compact features located about 75 million KM from the centre of the black hole. According to Katie Bouman, assistant professor of computing and mathematical sciences, electrical engineering, and astronomy at Caltech, this is the first three-dimensional reconstruction of gas rotating close to a black hole. Their paper was published in Nature Astronomy.

Aviad Levis, a postdoctoral scholar in Bouman’s group and lead author of the new paper, believes the model is a reconstruction based on our models of black hole physics. However, because it relies on these models’ accuracy, there is still a lot of uncertainty associated with it.

Leveraging AI

They also had to develop new computational imaging to reconstruct the 3D image. In June 2021, the team first considered whether it would be possible to create a 3D video of flares around a black hole. The Event Horizon Telescope (EHT) Collaboration, of which Bouman and Levis are members, had already published the first image of the supermassive black hole at the core of a distant galaxy called M87 and was working to do the same with EHT data from Sgr A*.

Pratul Srinivasan of Google Research, a co-author on the new paper, helped develop a technique known as neural radiance fields (NeRF) that was then just starting to be used by researchers; it has since significantly impacted computer graphics.

The team attempted to build on these recent developments in neural network representation; they could reconstruct the 3D environment around a black hole. Their biggest limitation was that from Earth, as anywhere, we only get a single viewpoint of the black hole.

Furthermore, they built. A version of NeRF that considers how gas moves around black holes. However, it must also consider how light bends around massive objects like black holes. They used ALMA for real data. ALMA is one of the most powerful radio telescopes in the world. However, because of the vast distance to the galactic centre, even ALMA does not have the resolution to see Sgr A*’s immediate surroundings. ALMA measures light curves, essentially videos of a single flickering pixel, which are created by collecting all of the radio-wavelength light detected by the telescope for each moment of observation.

Further developments

To figure out a likely 3D structure that explained the observations, the team developed an updated version of its method that not only incorporated the physics of light bending and dynamics around a black hole but also the polarised emission expected in hot spots orbiting a black hole. This technique represents each potential flare structure as a continuous volume using a neural network. This allows the researchers to computationally progress the initial 3D structure of a hotspot over time as it orbits the black hole to create a whole light curve.

The result is a video showing the clockwise movement of two compact, bright regions that trace a path around the black hole. The researchers state that it didn’t have to come out this way. There could have been arbitrary brightness scattered throughout the volume. It is very exciting that this looks a lot like the flares that computer simulations of black holes predict.

The scientists note that this is just the beginning of this exciting technology. They hope astronomers could use it on other rich time-series data to shed light on the complex dynamics of other such events and draw new conclusions.

Caltech News

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