Machine learning reveals how black holes grow


Even though they may appear to be completely unrelated, black holes and Las Vegas have one thing in common: whatever occurs there stays there, much to the chagrin of astrophysicists who are attempting to understand how, when, and why black holes originate and expand. The event horizon, which surrounds black holes, is a mysterious, unseen barrier from which nothing can escape, including matter, light, or information. Every trace of the black hole's history is absorbed by the event horizon.

According to Peter Behroozi, an associate professor at the University of Arizona Steward Observatory and a project researcher at the National Astronomical Observatory of Japan, "because of these physical realities, it had been deemed impossible to quantify how black holes evolved."

Behroozi co-led an international team with Steward doctoral student Haowen Zhang to rebuild the development histories of black holes using machine learning and supercomputers, successfully peeled back their event boundaries to show what is beyond.

Millions of artificial "universes" were simulated, and the results showed that supermassive black holes grew at the same pace as their host galaxies. Scientists had a theory about this for 20 years, but until recently, they had not been able to prove it. The team's research was reported in a publication in Monthly Notices of the Royal Astronomical Society.

According to Behroozi, a co-author on the article, "if you go back to earlier and earlier eras in the cosmos, you discover that exactly the same link was present." So, in the same way that we now observe in galaxies across the universe, as the galaxy develops from tiny to huge, its black hole is also growing from small to enormous.

It is believed that a supermassive black hole is present at the heart of the most, if not all, of the galaxies distributed throughout the cosmos. Many of these black holes have masses that are millions or even billions of times higher than that of the sun. How these behemoths expand as quickly as they do and how they arise in the first place has been one of astronomy' most puzzling mysteries.

Trinity is a platform developed by Zhang, Behroozi, and their colleagues that uses a novel type of machine learning to generate millions of different universes on a supercomputer, each of which adheres to a different physical theory for how galaxies should form. The goal of Trinity is to find answers. The researchers created a paradigm in which computers offer fresh hypotheses for the growth patterns of supermassive black holes. They then "watched" the virtual world to see whether it corresponded with decades of actual observations of black holes throughout the real universe. They utilized those principles to mimic the creation of billions of black holes in the virtual universe. The computers finally landed on the rule sets that best reflected observed data after millions of suggested and rejected rule sets.

Behroozi stated, "We're attempting to comprehend the laws governing how galaxies originate. "In essence, we ask Trinity to make educated guesses about possible physical rules, then we release them loose in a computer-generated universe to observe how that reality behaves. Does it resemble the genuine one in any way or not?"

The researchers claim that this method applies as well to all other objects in the cosmos, not just galaxies.

The name Trinity refers to the project's three main research domains: galaxies, their supermassive black holes, and their dark matter halos, which are enormous cocoons of dark matter that are invisible to direct measurements but whose existence is required to explain the physical properties of galaxies everywhere. Millions of galaxies and their dark matter halos were simulated using an older iteration of the researchers' architecture, known as the UniverseMachine, in past investigations. The researchers found that galaxies expanding in their dark matter haloes adhere to a very particular connection between the galaxy's mass and that of the halo.

In our latest research, Behroozi explained, "we added black holes to this connection and then investigated how black holes might grow in those galaxies to recreate all the findings people have made about them."

According to Zhang, the principal author of the study, "We have extremely excellent observations of black hole masses." "These are mainly limited to the local universe, though. It becomes increasingly challenging, and ultimately impossible, to assess the correlations between the masses of black holes and their host galaxies as you gaze farther away. Observations can't immediately tell us if that relationship remains true throughout the cosmos because of this ambiguity."

By combining data from millions of observed black holes at various phases of their evolution, Trinity enables astrophysicists to get over not just that restriction but also the event horizon information barrier for individual black holes. The researchers could assess the average development history of all black holes when they were considered as a whole, despite not being able to recreate the history of any particular black hole.

"You can compare the generated universe to all the observations of genuine black holes that we have," Zhang said. "You can do this by adding black holes into the simulation galaxy and entering rules about how they evolve." The appearance of every black hole and galaxy in the universe, dating all the way back to the creation of the universe, may then be recreated.

The simulations also shed light on another mysterious phenomenon: supermassive black holes, like the one at the center of the Milky Way, grew most rapidly in their early stages when the universe was only a few billion years old, but then their growth rate dramatically slowed down over the subsequent 10 billion years or so.

"We've known for a long that galaxies have this peculiar behavior, where their rate of new star formation reaches a peak, then it declines over time, and finally, later on, they cease generating stars completely," Behroozi said. "Now, we can demonstrate that black holes behave similarly, expanding and contracting in tandem with their host galaxies. This supports a long-held theory concerning the development of black holes in galaxies."

But the outcome raises further queries, he continued. Black holes are significantly more compact than the galaxies they inhabit. The supermassive black hole in the Milky Way would be the size of the period at the end of this sentence if it were shrunk to Earth's size.

Gas fluxes at many different sizes must be coordinated in order for the black hole to double in mass within the same time period as the bigger galaxy. It is still unclear how black holes and galaxies work together to strike this delicate balance.

The truly innovative aspect of Trinity, according to Zhang, is that it gives us a mechanism to determine what kinds of linkages between black holes and galaxies are consistent with a wide range of various datasets and observational techniques. "The technique enables us to identify precisely those associations between dark matter haloes, galaxies, and black holes that can account for all of the observed data. The message is essentially, "OK, given all these data, we know the relationship between galaxies and black holes must look like this, rather than like that." And that strategy is quite effective."

Machine learning reveals how black holes grow Machine learning reveals how black holes grow Reviewed by Blogger on December 21, 2022 Rating: 5
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