Using OpenAI Codex to Accelerate Black Hole Plasma Simulations

AI-Driven Algorithm Discovery for Black Hole Simulations

Astrophysicist Chi-kwan Chan is utilizing OpenAI Codex to develop new numerical algorithms that allow for more realistic simulations of plasma around supermassive black holes. By using AI to propose candidate mathematical schemes, researchers can bypass the computational bottlenecks that have historically limited the scale and accuracy of black hole modeling.

Overcoming Computational Bottlenecks in Plasma Modeling

Simulating the environment around a black hole requires modeling plasma—superheated matter consisting of electrically charged electrons and ions. While dense plasma can be modeled as a fluid, the hot and diffuse regions near supermassive black holes behave differently, where particles rarely collide and instead spiral around magnetic field lines.

To model this behavior accurately, standard simulations must track trillions of particles as they corkscrew around the black hole. This requires computers to take extremely small timesteps to calculate every single rotation, which consumes the majority of supercomputing power and prevents researchers from studying larger-scale behaviors.

The Role of Codex in Algorithmic Exploration

Chi-kwan Chan uses Codex to accelerate the exploration of mathematical techniques that change how simulations track particle motion. The goal is to find a method that allows the computer to simulate the movement of particles without needing to follow every tiny spiral directly.

Iterative Testing and Verification

Rather than relying on AI as a "black box," Chan's group uses Codex to propose and implement numerical schemes that are fully inspectable and physically understandable. The process follows a rigorous scientific framework:

• Candidate Generation: Codex generates multiple potential mathematical approaches to solve the problem.
• Testing: Each proposed algorithm is tested against known solutions to verify its accuracy.
• Verification: Because scientific ideas are subject to repeated testing, the AI-generated suggestions are treated as hypotheses that must be proven through verification and reproducibility.

Implications for Astrophysics

If the successful implementation of these Codex-assisted algorithms is achieved, scientists will be able to simulate trillions of particles around black holes. This capability would allow the Event Horizon Telescope (EHT) collaboration—which produced the first image of a black hole in 2019—to move beyond still images toward producing the first video of a supermassive black hole, specifically focusing on the one at the center of the M87 galaxy.