How an Astrophysicist Uses Artificial Intelligence to Simulate Black Holes

The gravity around a black hole is so extreme that nothing— not even light—can escape after getting close enough. To study these fascinating and terrifying objects of the universe, astrophysicists like Chi-kwan Chan rely on sophisticated computational simulations and cutting-edge astronomical observations. Now, with the help of Codex, OpenAI's programming-focused AI tool, Chan is breaking barriers that seemed insurmountable for modern science.

Who is Chi-kwan Chan?

Chi-kwan Chan is a researcher at the University of Arizona and the Steward Observatory, and he is part of the renowned international collaboration Event Horizon Telescope (EHT)—the scientific project responsible for capturing, in 2019, the first image of a black hole in human history. Since then, the team has continued to enhance its instruments and observational capabilities with an even more ambitious goal: to produce the first video of a supermassive black hole, focusing on what is at the center of the M87 galaxy.

But transforming astronomical observations into scientific understanding requires monumental data processing, large-scale computational workflows, and simulations capable of modeling some of the most extreme physics in the universe.

Why are Black Holes So Important to Science?

According to Chan, black holes are among the best natural laboratories to test Einstein's theory of general relativity. This theory is currently the best explanation we have for gravity: instead of being a force that attracts objects, gravity results from mass and energy curving the fabric of spacetime.

Since light cannot escape from a black hole, scientists study the region around it called the event horizon—a boundary beyond which matter cannot return. "It's a point of no return," Chan explained. The matter rotating just outside this boundary emits light that astrophysicists can observe, measure, and simulate.

The image released by the EHT in 2019 showed exactly this: the shadow of a black hole embedded in glowing plasma near the event horizon. Chan helped develop the simulation and computing tools that the team used to interpret these historic observations.

The Great Challenge: Modeling Plasma

One of the biggest challenges for Chan and his team is modeling the plasma around black holes. Plasma is an overheated matter composed of electrons and electrically charged ions.

In many simulations, scientists simplify plasma by treating it like a fluid, using well-established equations to model its movement around a black hole. This approach works reasonably well in denser plasma regions where electrons and ions are constantly colliding with each other.

But there is a serious problem:

• Near the supermassive black holes studied by Chan, some regions become so hot and diffuse that the particles rarely encounter one another.
• "They don't collide with each other," the researcher explained.
• Instead, the particles spiral around magnetic field lines, a behavior radically different from the conventional fluid model.

This discrepancy between the simplified model and physical reality requires much more complex algorithms—and this is precisely where artificial intelligence comes into play.

How Codex is Transforming Research

Codex, developed by OpenAI, is an AI model trained to understand and generate programming code. For an astrophysicist like Chan, who constantly needs to write, revise, and optimize extremely complex algorithms, the tool represents an unprecedented leap in productivity.

With Codex, Chan can:

• Accelerate the development of new simulation algorithms without needing to write each line of code manually;
• Explore alternative approaches to model plasma behavior in extreme physics regimes;
• Debug and optimize existing codes more efficiently, saving precious research hours;
• Translate abstract scientific ideas into functional computational implementations much more swiftly.

The AI acts as a scientific co-pilot: it does not replace the specialized knowledge of the astrophysicist but amplifies his ability to turn hypotheses into concrete computational experiments.

From Static Image to the First Video of a Black Hole

The next major milestone for the EHT collaboration is ambitious: to create the first video of a supermassive black hole. For this, the team is collecting new observations and enhancing its simulations. The focus is on the black hole at the center of the M87 galaxy, but the methodology also applies to Sagittarius A*, the black hole at the center of our own Milky Way.

Simulations generated by supercomputers already show the movement of plasma around these celestial objects. However, for the final video to be scientifically accurate and consistent with real observations, the simulations need to incorporate increasingly sophisticated plasma models—exactly the problem Chan is tackling with Codex's support.

AI as the Scientific Tool of the Future

Chi-kwan Chan's case illustrates a growing trend in cutting-edge science: artificial intelligence as an indispensable partner in scientific research. It’s not just about automating repetitive tasks but empowering scientists to tackle problems that would otherwise take years longer to solve.

Some implications of this partnership include:

1. Democratization of scientific computing: researchers with fewer computational resources can develop more sophisticated simulations with the help of AI.
2. Acceleration of the discovery cycle: from hypothesis to simulated result, the time is drastically reduced.
3. New types of physical models: AI can suggest approaches that a human scientist might not have considered in isolation.

Conclusion

The boundary between theoretical physics and computation has never been so thin. With OpenAI's Codex, astrophysicists like Chi-kwan Chan are pushing the limits of what is possible to simulate—and, consequently, what is possible to understand about the most mysterious and powerful objects in the universe.

As humanity prepares to see, for the first time, a black hole in motion—not as a static photograph, but as a living film of the cosmos—it becomes clear that artificial intelligence is not just the future of technology. It is also the future of science.