Speaker
Description
As particle collider experiments produce increasingly large and complex datasets, a fundamental question arises: how should we quantify the similarity between two events? A variety of physically motivated metrics have been developed—from optimal transport to phase-space distances—yet the geometric structures induced by these metrics remain largely unexplored. We introduce the Multi-Reference Relative Representation (M3R), a framework that embeds diverse distances into a common coordinate space, thus enabling a systematic study of collider event manifolds. Using M3R, we investigate the geometry of single-event manifolds and the decision boundaries separating distinct physical processes. Further, multiple metrics are combined into unified event representations that capture complementary information. Our M3R framework provides an interpretable geometric language for organizing metric spaces, opening the possibility of a unified study of collider event geometry across analytical and data-driven approaches.