Research led by Peter Behroozi (Steward Observatory) and graduate student Haowen Zhang is leveraging supercomputing power and machine learning to recontruct the growth histories of black holes starting at their event horizons. Along with an international team, Behroozi and Zhang are creating millions of simulated “universes” to test astrophysical predictions of black hole origins. The research confirmed a long-held theory that black holes grow in parallel to their host galaxies. See UA News science write-up on Behroozi and Zhang’s work.

“We’ve known for a while that galaxies have this strange behavior, where they reach a peak in their rate of forming new stars, then it dwindles over time, and then, later on, they stop forming stars altogether,” Behroozi said. “Now, we’ve been able to show that black holes do the same: growing and shutting off at the same times as their host galaxies. This confirms a decades-old hypothesis about black hole growth in galaxies.”

Behroozi, Zhang, and colleagues created Trinity, a platform that uses a novel form of machine learning that generates millions of simulated galaxies on a supercomputer each with different physical characteristics. The tool is then used to apply a framework in which the computer proposes new rules for how black holes grow over time which was then used to billions of simulations allowing comparison with actual observations across the real universe.

“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 different datasets and observational methods,” Zhang said.

The team’s findings have been published in Monthly Notices of the Royal Astronomical Society.

Image credit: H. Zhang, Wielgus et al. (2020), ESA/Hubble & NASA, A. Bellini