AI Uncovers Wild Spin of the Milky Way’s Supermassive Black Hole

Using millions of simulations and AI, astronomers discovered the Milky Way’s black hole spins near top speed.

The breakthrough, supported by four decades of distributed computing innovation, hints at magnetic behaviors that contradict long-held theories.

AI Peers Into the Heart of the Milky Way

A team of international astronomers has used artificial intelligence and millions of simulated models to unlock new secrets about black holes, especially the one at the center of our own galaxy. Thanks to this cutting-edge approach, they’ve discovered that the Milky Way’s central black hole, known as Sagittarius A*, is spinning at nearly its maximum possible speed.

To make this breakthrough, the scientists trained a neural network using an enormous set of synthetic black hole simulations. These were powered by high-throughput computing technology from the Center for High Throughput Computing (CHTC), a joint effort by the Morgridge Institute for Research and the University of Wisconsin–Madison. Their findings and methods were published in three new studies in the journal Astronomy & Astrophysics.

Distributed Computing: The Science Engine

This kind of high-throughput computing is no ordinary setup. It was pioneered 40 years ago by Wisconsin computer scientist Miron Livny and works by distributing massive tasks across thousands of computers. Imagine turning a single, towering challenge into a swarm of smaller, faster problems being solved at once. This system is now a vital tool for scientific discovery, helping researchers around the world tackle big questions—from dark matter and gravitational waves to antibiotic resistance.

Back in 2019, the Event Horizon Telescope (EHT) team revealed the first-ever image of a supermassive black hole in the galaxy M87. In 2022, they followed up with the iconic image of Sagittarius A* at the heart of the Milky Way. While these images were groundbreaking, the data behind them held even deeper insights that were hard to decode.

Neural Networks Meet Black Hole Physics

Previous studies by the EHT Collaboration used only a handful of realistic synthetic data files. Funded by the National Science Foundation (NSF) as part of the Partnership to Advance Throughput Computing (PATh) project, the Madison-based CHTC enabled the astronomers to feed millions of such data files into a so-called Bayesian neural network, which can quantify uncertainties. This allowed the researchers to make a much better comparison between the EHT data and the models.

Thanks to the neural network, the researchers now suspect that the black hole at the center of the Milky Way is spinning at almost top speed. Its rotation axis points to the Earth. In addition, the emission near the black hole is mainly caused by extremely hot electrons in the surrounding accretion disk and not by a so-called jet. Also, the magnetic fields in the accretion disk appear to behave differently from the usual theories of such disks.

“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.”

Scaling to Millions of 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.

Four Decades of Distributed Discovery

“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.”

References:

“Deep learning inference with the Event Horizon Telescope – I. Calibration improvements and a comprehensive synthetic data library” by M. Janssen, C.-k. Chan, J. Davelaar, I. Natarajan, H. Olivares, B. Ripperda, J. Röder, M. Rynge and M. Wielgus, 6 June 2025, Astronomy & Astrophysics.
DOI: 10.1051/0004-6361/202553784

“Deep learning inference with the Event Horizon Telescope – II. The ZINGULARITY framework for Bayesian artificial neural networks” by M. Janssen, C.-k. Chan, J. Davelaar and M. Wielgus, 6 June 2025, Astronomy & Astrophysics.
DOI: 10.1051/0004-6361/202553785

“Deep learning inference with the Event Horizon Telescope – III. ZINGULARITY results from the 2017 observations and predictions for future array expansions” by M. Janssen, C.-k. Chan, J. Davelaar and M. Wielgus, 6 June 2025, Astronomy & Astrophysics.
DOI: 10.1051/0004-6361/202553786

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