Machine learning could reveal how black holes grow



Black holes and Las Vegas share one thing in common, despite their apparent disconnection: Astrophysicists trying to figure out how, when, and why black holes form and grow are dissatisfied because everything that happens there stays there.

Black holes are surrounded by an unidentified layer known as the event horizon, which serves as the boundary beyond which no matter, light, or information can pass. The event horizon takes in every trace of the past of the black hole.

Peter Behroozi, an associate professor at the University of Arizona Steward Observatory and a project researcher at the National Astronomical Observatory of Japan, was quoted by the Arizona University as saying, “Because of these physical facts, it had been thought impossible to measure how black holes formed.”

With Steward doctoral student Haowen Zhang, Behroozi co-led a global team that used machine learning and supercomputers to rebuild the growth histories of black holes. They were successful in peeling back their event boundaries to reveal what lies beyond.

The results of simulating millions of artificial “universes” demonstrated that supermassive black holes grew at the same rate as their host galaxies. For 20 years, scientists had a theory about this, but they couldn’t prove it until recently. A report on the team’s research appeared in the Royal Astronomical Society’s Monthly Notices.

According to a quote from Behroozi, “If you go back to earlier and earlier times in the universe, you find that exactly the same relationship was present.” As a result, just as we observe in galaxies today and throughout the universe, the black hole in the galaxy is also expanding from its original size.

Most, if not all, of the galaxies in the universe are thought to contain a supermassive black hole at their centers. The masses of many of these black holes are millions or even billions of times greater than those of the sun. One of the most perplexing mysteries in astrophysics is the origin and rapid expansion of these gigantic objects.

Trinity is a platform developed by Zhang, Behroozi, and their coworkers that employs a novel type of machine learning to generate millions of distinct universes on a supercomputer, each of which adheres to a distinct physical theory for how galaxies ought to form. Trinity’s objective is to respond.

Computers provide novel hypotheses for the growth patterns of supermassive black holes, as the researchers developed a paradigm. After that, they “watched” the virtual universe to see if it matched decades of real-world observations of black holes.

By applying those principles, the team was able to imitate the expansion of billions of black holes in the virtual universe. After millions of proposed and rejected rule sets, the computers came to the rule sets that best described the observed data.

The researchers assert that this approach is applicable to all other objects in the universe in addition to galaxies.

Galaxies, their supermassive black holes, and their dark matter halos are the three primary research areas of the project. Dark matter halos are enormous cocoons of dark matter that are not visible to direct measurements but are necessary for understanding the physical properties of galaxies everywhere.

These three fields of study are referred to as the Trinity. In previous studies, the UniverseMachine, an older version of the researchers’ framework, was used to simulate millions of galaxies and their dark matter halos.

The researchers discovered that the galaxy’s mass and the halo’s mass follow a very specific relationship when galaxies expand in their dark matter haloes.

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