Linearized optimal transport for collider events
Linearized optimal transport for collider events
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DOI：
10.1103/physrevd.102.116019
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发表时间：
2020-12-29
影响因子：
5
通讯作者：
Craig, Katy
中科院分区：
文献类型：
--
作者：
Cai, Tianji;Cheng, Junyi;Craig, Katy
我们介绍了一个使用线性化最优传输(LOT)工具来计算碰撞事件之间距离的有效框架。这保留了最近引入的Energy mover距离的许多优势，它量化了将一个活动重新安排到另一个活动所需的工作，同时显著降低了计算成本。它还提供了服从于简单的机器学习算法和可视化技术的欧几里德嵌入，我们在各种喷气标签示例中演示了这一点。LOT近似降低了最优输运理论在对撞机物理中的不同应用门槛。
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.