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arXiv · 2008.08604

Linearized Optimal Transport for Collider Events

Tianji Cai·Junyi Cheng·Katy Craig·Nathaniel Craig
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Abstract

We introduce an efficient framework for computing the distance between collider events using the tools of Linearized Optimal Transport (LOT). This preserves many of the advantages of the recently-introduced Energy Mover's Distance, which quantifies the "work" required to rearrange one event into another, while significantly reducing the computational cost. It also furnishes a Euclidean embedding amenable to simple machine learning algorithms and visualization techniques, which we demonstrate in a variety of jet tagging examples. The LOT approximation lowers the threshold for diverse applications of the theory of optimal transport to collider physics.

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Tianji Cai, Junyi Cheng, Katy Craig, Nathaniel Craig. 2020-08-19. Linearized Optimal Transport for Collider Events. https://doi.org/10.1103/physrevd.102.116019

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