Linearized Optimal Transport for Collider Events

Understand

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.