Is Our Black Hole Defying Physics? New AI Study Challenges Theories

Astronomers, using AI and high-throughput computing from the University of Wisconsin-Madison's CHTC, have unlocked new insights into Sagittarius A* - the supermassive black hole at the heart of our galaxy. By training a neural network on millions of simulations, researchers found the black hole is spinning near its maximum speed, with its axis of rotation aimed toward Earth. The findings are based on data from the Event Horizon Telescope and offer fresh understanding of black hole behaviour. 

The AI also suggests that the emission near the black hole is primarily from extremely hot electrons in the accretion disk rather than a jet, and that the magnetic fields in the disk behave differently than previously thought. This research, published in Astronomy & Astrophysics, was made possible by high-throughput computing, a distributed computing method pioneered by Miron Livny, which allowed researchers to process a massive amount of data efficiently.

"That we are defying the prevailing theory is, of course, exciting," says lead researcher Michael Janssen, of Radboud University Nijmegen, the Netherlands. "However, I see our AI and machine learning approach primarily as a first step. Next, we will improve and extend the associated models and simulations."

"The ability to scale up to the millions of synthetic data files required to train the model is an impressive achievement," adds Chi-kwan Chan, an Associate Astronomer of Steward Observatory at the University of Arizona and a longtime PATh collaborator. "It requires dependable workflow automation and effective workload distribution across storage resources and processing capacity."

"We are pleased to see EHT leveraging our throughput computing capabilities to bring the power of AI to their science," says Professor Anthony Gitter, a Morgridge Investigator and a PATh Co-PI. "Like in the case of other science domains, CHTC's capabilities allowed EHT researchers to assemble the quantity and quality of AI-ready data needed to train effective models that facilitate scientific discovery."

The NSF-funded Open Science Pool, operated by PATh, offers computing capacity contributed by more than 80 institutions across the United States. The Event Horizon black hole project performed more than 12 million computing jobs in the past three years.

"A workload that consists of millions of simulations is a perfect match for our throughput-oriented capabilities that were developed and refined over four decades", says Livny, director of the CHTC and lead investigator of PATh. "We love to collaborate with researchers who have workloads that challenge the scalability of our services."