Foundation Models for Collider Physics: AI as a Tool for Discovery

by Dr Yulei Zhang (University of Washington)

Wednesday 24 Sept 2025, 12:00 → 13:30 Asia/Shanghai

5#/6th-603 - Meeting Room 603 (Science Building)

Abstract:

Experiments at the Large Hadron Collider produce some of the most complex datasets in science. A central challenge in high-energy physics is to extract rare signals, reconstruct invisible particles such as neutrinos, and search for subtle deviations from the Standard Model with maximal sensitivity. These are fundamentally physics problems, but their scale and complexity call for new strategies.
In this talk, I will present EveNet, a large-scale foundation model developed for collider physics. Trained on billions of simulated events, it provides a common starting point for diverse analysis tasks, including improving search sensitivity, reconstructing hidden structures, and detecting unexpected anomalies. By integrating such methods into the physicist’s toolkit, we show how foundation models can accelerate discovery and open new directions in the exploration of fundamental laws of nature.

Biography:

Dr. Yulei Zhang is a postdoctoral researcher at the University of Washington, a UW Data Science Postdoctoral Fellow, and a member of the A3D3 Institute. His research focuses on experimental particle physics, particularly Higgs physics and searches for new phenomena beyond the Standard Model. Within the ATLAS Collaboration, he serves as convenor of the Non-Resonant Multilepton subgroup and leads several di-Higgs analyses. His current efforts center on developing foundation models and advanced machine-learning methods to accelerate physics discovery, alongside quantum information studies at colliders and contributions to the FAIR Universe project on large-scale ML benchmarks for uncertainty quantification.

Host: Prof. Liang Li

Alternative online link：https://meeting.tencent.com/dm/WrHy1ttfEf8h

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