OpenAI’s Codex is helping astrophysicist Chi-kwan Chan refine algorithms to simulate the movement of plasma around black holes. Chan, a researcher at the University of Arizona and Steward Observatory, is working to improve computational models that capture the complex physics of these extreme environments. The goal is to enhance the realism of simulations that study how matter behaves near black holes, which are key to testing Einstein’s general theory of relativity. Source: openai
Chan’s team faces challenges in modeling plasma, which is superheated matter composed of charged electrons and ions. In many simulations, scientists treat plasma as a fluid, using equations that work well in denser regions. However, near supermassive black holes, plasma becomes so hot and diffuse that particles rarely collide. Instead, they spiral around magnetic field lines, requiring simulations to track trillions of particles. Standard simulations must calculate each tiny motion, which limits the ability to study larger-scale behavior. Source: openai
Chan is using Codex to derive candidate algorithms that can model these complex interactions without tracking every particle movement. The AI system generates potential approaches, which Chan and his colleagues test against known solutions. While large language models can make mistakes, Chan emphasizes the importance of rigorous testing in scientific research. He believes AI can accelerate discovery while maintaining reproducibility and verification. Source: openai
