Speaker
Description
AI for fundamental physics is now a burgeoning field, with numerous efforts pushing the boundaries of experimental and theoretical physics, as well as machine learning research itself. In this talk, I will introduce a recent innovative application of Natural Language Processing to the state-of-the-art precision calculations in high energy particle physics. Specifically, we use Transformers to predict symbolic mathematical expressions that represent scattering amplitudes in planar N=4 Super Yang-Mills theory—a quantum field theory closely related to the real-world QCD at the Large Hadron Collider. Our first results have demonstrated great promises of Transformers for amplitude calculations, while its major challenges are being addressed by ongoing work. This study opens the door for an exciting new scientific paradigm where discoveries and human insights are inspired and aided by an AI agent.
References
https://arxiv.org/pdf/2405.06107
Significance
This is the first application of large language model in computing scattering amplitudes in a simplified theory of QCD. The work shows that AI methods are also applicable to symbolic data in the context of high-precision theoretical calculations, and I'll also discuss work-in-progress on how to improve the current framework to march towards a future with automatic AI amplitude oracles.
