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Abstract：
As the Large Hadron Collider (LHC) generates hundreds of petabytes of data and even more with its high-luminosity upgrade, particle physics is entering a new era of data-driven discovery where machine learning (ML) techniques play a pivotal role. Alongside numerous task-specific ML algorithms, recent works have introduced foundation models excelling across diverse applications. At the heart of these ML models, especially general-purpose ones, is a geometric representation space for collider events that encodes the essential physics. Key questions then arise: How can we probe and refine the representation space for theoretical insights?
This talk presents a first step towards the construction and analysis of a collider space. I will introduce two metric structures, one inspired by the mathematical theory of optimal transport and the other grounded in the physical phase space. Such explicitly-defined metrics enable comparisons with the representation space implicitly generated by an ML model. This paves the way to further dissect a model’s internals and offers hope for discovering new physical laws directly from data.
Biography:
Tianji Cai (蔡恬吉) is currently 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 the 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 tools to aid theoretical understanding of AI systems.
Host: Prof. Yifeng Sun
Alternative online link: https://meeting.tencent.com/dm/WPkWKKlImL2e (id: 631313099 passcode: 123456）