Tianji Cai (Tongji University), "A Cookbook for Collider Metrics: Understanding, Comparing & Combining Event Distances"

Friday 3 Jul 2026, 15:00 → 16:00 Asia/Seoul

CTPU Seminar room (Theory Bldg, 4F)

As particle collider experiments continue to produce ever larger and more complex datasets, a fundamental question arises: how should we measure the similarity between two collider events? A physically meaningful notion of distance lies at the heart of a wide range of applications, from jet tagging to anomaly detection. More fundamentally, it provides a geometric language for collider physics, serving as a common framework for connecting physics-inspired observables with modern machine learning.

In this talk, I will present a practical “cookbook” for collider event metrics. Starting from three representative metrics based on optimal transport and relativistic N-body phase space, I will discuss the physical principles encoded by different metrics, how they can be compared on an equal footing, and what aspects of collider events each captures. Finally, I will explore how complementary collider metrics may be combined into a unified framework for event geometry. Beyond providing new tools for collider phenomenology, such a framework offers a principled foundation for understanding, comparing, and designing physics-aware AI models, illustrating how particle physics can serve as a unique testbed for the development of Scientific AI.