SHAPER: can you hear the shape of a jet?
SHAPER: can you hear the shape of a jet?
复制标题
DOI：
10.1007/jhep06(2023)195
复制
发表时间：
2023-02
影响因子：
5.4
通讯作者：
Demba E. Ba;Akshunna S. Dogra;Rikab Gambhir;Abiy Tasissa;J. Thaler
中科院分区：
文献类型：
--
作者：
Demba E. Ba;Akshunna S. Dogra;Rikab Gambhir;Abiy Tasissa;J. Thaler
识别喷流中感兴趣的子结构是寻找新物理和探索对撞机标准模型的重要工具。这些子结构工具中的许多以前已经被证明采用最优运输问题的形式，特别是能量传送器的距离(EMD)。在这项工作中，我们证明了EMD实际上是比较对撞机事件的自然结构，这解释了它最近在理解事件和喷射子结构方面取得的成功。然后，我们提出了一种基于参数能量重构的形状搜索算法(S Haper)，这是一个定义和计算基于形状的观测量的通用框架。S·哈珀将N-射流性从点群推广到任何扩展的、可参数化的形状。这是通过有效地最小化事件和表示理想化形状的能量流的参数化流形之间的EMD来实现的，该流形使用瓦瑟斯坦度量的双势Sinkhorn近似来实现。我们展示了如何使用作为流形的可观测对象的几何语言来定义具有内置红外线和共线安全性的新型可观测对象。我们用几个新的基于形状的观测值的例子进行了喷流子结构的经验研究，从而证明了S·哈珀框架的有效性。
The identification of interesting substructures within jets is an important tool for searching for new physics and probing the Standard Model at colliders. Many of these substructure tools have previously been shown to take the form of optimal transport problems, in particular the Energy Mover’s Distance (EMD). In this work, we show that the EMD is in fact the natural structure for comparing collider events, which accounts for its recent success in understanding event and jet substructure. We then present a Shape Hunting Algorithm using Parameterized Energy Reconstruction (S haper), which is a general framework for defining and computing shape-based observables. S haper generalizes N-jettiness from point clusters to any extended, parametrizable shape. This is accomplished by efficiently minimizing the EMD between events and parameterized manifolds of energy flows representing idealized shapes, implemented using the dual-potential Sinkhorn approximation of the Wasserstein metric. We show how the geometric language of observables as manifolds can be used to define novel observables with built-in infrared-and-collinear safety. We demonstrate the efficacy of the S haper framework by performing empirical jet substructure studies using several examples of new shape-based observables.