Scattering Amplitudes: A New Playground for Machine Learning

by Dr Tianji 恬吉 Cai蔡 (SLAC National Accelerator Laboratory)

Friday 25 Apr 2025, 11:00 → 12:00 Asia/Shanghai

B105 (CHEP)

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.

Bio: Tianji Cai (蔡恬吉) is a postdoctoral research associate in the Fundamental Physics Directorate at the SLAC National Accelerator Laboratory, and a research affiliate at the Lawrence Berkeley National Laboratory. She obtained her Ph.D. degree in 2023 at University of California, Santa Barbara, and holds two bachelor's degrees from Duke University and Shanghai Jiao Tong University. Her research interest lies at the intersection of High Energy Theory (HEP) and Artificial Intelligence (AI), with a focus on developing novel machine learning frameworks for collider phenomenology and scattering amplitudes. She’s also interested in using HEP to aid theoretical understanding of AI systems.

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