Linearized Optimal Transport for Collider Events

• URL: http://arxiv.org/abs/2008.08604v1
• Date: Wed, 19 Aug 2020 18:00:09 GMT
• Title: Linearized Optimal Transport for Collider Events
• Authors: Tianji Cai, Junyi Cheng, Katy Craig, Nathaniel Craig
• Abstract summary: We introduce an efficient framework for computing the distance between collider events using the tools of Linearized Optimal Transport (LOT) It also furnishes a Euclidean embedding amenable to simple machine learning algorithms and visualization techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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