Machine learning reveals how black holes grow

Machine learning reveals how black holes grow

Machine learning reveals how black holes grow

How it works: Through trial and error, machine learning tests many different pairings of simulated galaxies and black holes created using different rules, then chooses the pairing that best matches real astronomical observations. Credit: H. Zhang, Wielgus et al. (2020), ESA/Hubble & NASA, A. Bellini

As different as they may seem, black holes and Las Vegas have one thing in common: what happens there stays there, much to the frustration of astrophysicists trying to understand how, when and why black holes form and grow up.

Black holes are surrounded by a mysterious and invisible layer – the event horizon – from which nothing can escape, be it matter, light or information. The event horizon swallows every bit of evidence about the black hole’s past.

“Because of these physical facts, it had been deemed impossible to measure the formation of black holes,” said Peter Behroozi, associate professor at the University of Arizona’s Steward Observatory and project researcher at the Observatory. National Astronomy of Japan.

Along with Steward PhD student Haowen Zhang, Behroozi led an international team to use machine learning and supercomputers to reconstruct black hole growth histories, effectively peeling back their event horizons to reveal what lies beyond. .

Computer-generated simulations of millions of “universes” have revealed that supermassive black holes grow in parallel with their host galaxies. This had been suspected for 20 years, but scientists had not been able to confirm this relationship until now. An article containing the team’s findings has been published in Royal Astronomical Society Monthly Notices.

“If you go back to earlier and earlier times in the universe, you find that exactly the same relationship was present,” said Behroozi, co-author of the paper. “So as the galaxy grows from small to large, its black hole also grows from small to large, in exactly the same way we see it in today’s galaxies all over the universe.”

Most, if not all, of the galaxies scattered throughout the cosmos are believed to harbor a supermassive black hole at their center. These black holes have masses greater than 100,000 times that of the sun, with some having millions or even billions of solar masses. One of the trickiest questions in astrophysics has been how these behemoths grow as fast as they do and how they form in the first place.

To find answers, Zhang, Behroozi and their colleagues created Trinity, a platform that uses a new form of machine learning capable of generating millions of different universes on a supercomputer, each obeying different physical theories about how which galaxies should form. The researchers built a framework in which computers come up with new rules for how supermassive black holes grow over time.

They then used these rules to simulate the growth of billions of black holes in a virtual universe and “observed” the virtual universe to test whether it matched decades of actual observations of black holes in the real universe. After millions of proposed and rejected rule sets, the computers settled on the rules that best described the existing observations.

“We are trying to understand the rules of galaxy formation,” Behroozi said. “In a nutshell, we make Trinity guess what physical laws may be and let them go into a simulated universe and see how that universe turns out. Does it look like the real thing or not?”

According to the researchers, this approach works equally well for anything else inside the universe, not just galaxies.

The name of the project, Trinity, refers to its three main fields of study: galaxies, their supermassive black holes and their dark matter halos, vast cocoons of dark matter invisible to direct measurements but whose existence is necessary to explain the physical characteristics. galaxies everywhere. In previous studies, the researchers used an earlier version of their framework, called UniverseMachine, to simulate millions of galaxies and their dark matter halos. The team found that galaxies growing in their dark matter halos follow a very specific relationship between the mass of the halo and the mass of the galaxy.

“In our new work, we added black holes to this relationship,” Behroozi said, “and then asked how black holes could grow in these galaxies to replicate all the observations people have made about them. .”

“We have very good observations of black hole masses,” said Zhang, the paper’s lead author. “However, these are largely limited to the local universe. As you look further out, it becomes increasingly difficult, if not impossible, to accurately measure the relationships between the masses of black holes and their host galaxies. Because of this uncertainty, observations may not tell us directly whether this relationship holds throughout the universe.”

Trinity allows astrophysicists to circumvent not only this limitation, but also the event horizon information barrier for individual black holes by assembling information from millions of observed black holes at different stages of their growth. Although no individual black hole history could be reconstructed, the researchers were able to measure the average growth history of all black holes taken together.

“If you put black holes in the simulated galaxies and enter rules about how they grow, you can compare the resulting universe to all the actual black hole observations we have,” Zhang said. “We can then reconstruct what any black hole and galaxy in the universe looked like from now until the very beginning of the cosmos.”

The simulations shed light on another puzzling phenomenon: Supermassive black holes, like the one found at the center of the Milky Way, grew most vigorously during their infancy, when the universe had no history. only a few billion years, only to slow considerably over the ensuing time, over the last 10 or so billion years.

“We’ve known for some time that galaxies have this weird behavior, where they peak in their rate of forming new stars, then it decreases over time, and then, later on, they stop forming stars altogether,” Behroozi said. “Now we have been able to show that black holes do the same thing: grow and die together with their host galaxies. This confirms a decades-old hypothesis about the growth of black holes in galaxies.”

However, the result raises more questions, he added. Black holes are much smaller than the galaxies in which they live. If the Milky Way were reduced to the size of Earth, its supermassive black hole would be the size of the period at the end of this sentence.

For the black hole to double in mass in the same time frame as the larger galaxy, there needs to be synchronization between gas flows at vastly different scales. How black holes conspire with galaxies to achieve this balance remains to be understood.

“I think the really original thing about Trinity is that it provides us with a way to find out what kind of connections between black holes and galaxies are consistent with a wide variety of datasets and observing methods. different,” Zhang said.

“The algorithm allows us to precisely select relationships between dark matter halos, galaxies and black holes that are able to replicate all of the observations that have been made. It basically tells us, ‘OK, given all these data, we know the link between galaxies and black holes must look like this, rather than that. And this approach is extremely powerful.”

More information:
Haowen Zhang (张昊文) et al, Trinity I: self-consistently modeling the halo-galaxy-supermassive dark matter black hole connection of z=0–10, Royal Astronomical Society Monthly Notices (2022). DOI: 10.1093/mnras/stac2633

Provided by the University of Arizona

Quote: Machine Learning Reveals Black Hole Growth (December 15, 2022) Retrieved December 21, 2022 from

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