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SUMMARY:Tianji Cai (Tongji University)\, "A Cookbook for Collider Metrics:
  Understanding\, Comparing & Combining Event Distances"
DTSTART:20260703T060000Z
DTEND:20260703T070000Z
DTSTAMP:20260715T075700Z
UID:indico-event-1258@indico.ibs.re.kr
DESCRIPTION:As particle collider experiments continue to produce ever larg
 er and more complex datasets\, a fundamental question arises: how should w
 e measure the similarity between two collider events? A physically meaning
 ful 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 f
 or connecting physics-inspired observables with modern machine learning.\n
 In this talk\, I will present a practical “cookbook” for collider even
 t metrics. Starting from three representative metrics based on optimal tra
 nsport and relativistic N-body phase space\, I will discuss the physical p
 rinciples encoded by different metrics\, how they can be compared on an eq
 ual 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 colli
 der phenomenology\, such a framework offers a principled foundation for un
 derstanding\, comparing\, and designing physics-aware AI models\, illustra
 ting how particle physics can serve as a unique testbed for the developmen
 t of Scientific AI.\n\nhttps://indico.ibs.re.kr/event/1258/
LOCATION:CTPU Seminar room (Theory Bldg\, 4F)
URL:https://indico.ibs.re.kr/event/1258/
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