Systematic quark/gluon identification with ratios of likelihoods
Systematic quark/gluon identification with ratios of likelihoods
复制标题
DOI：
10.1007/jhep12(2022)021
复制
发表时间：
2022-07
影响因子：
5.4
通讯作者：
S. Bright-Thonney;I. Moult;B. Nachman;S. Prestel
中科院分区：
文献类型：
--
作者：
S. Bright-Thonney;I. Moult;B. Nachman;S. Prestel
区分夸克和胶子引发的喷注一直是喷注子结构的中心焦点，导致引入了许多可观测量和高微扰精度的计算。与此同时，已经有许多尝试使用统计学和机器学习工具来充分利用射流辐射模式。我们提出了一种新的方法，它结合了对喷气子结构的深入分析理解与机器学习和统计学所承诺的最优性。在指定一个近似的全发射相空间，我们展示了如何构建一个给定的分类任务的最佳观察。这个过程证明了夸克和胶子射流的情况下，我们展示了如何系统地捕获分裂函数中的子程函校正，并证明加权多重性的线性组合是最佳的可观测。除了提供一个新的和强大的框架，系统地改善射流子结构观测，我们展示了几个夸克与胶子喷注标记观测的性能在部分子级蒙特卡罗模拟，并发现他们执行或接近的水平的深度神经网络分类器。结合最近在高阶部分子簇射发展方面的快速进展，我们相信，我们的方法为系统地利用大型强子对撞机（LHC）及以后的射流子结构分析中的次引导效应提供了基础。
Discriminating between quark-and gluon-initiated jets has long been a central focus of jet substructure, leading to the introduction of numerous observables and calculations to high perturbative accuracy. At the same time, there have been many attempts to fully exploit the jet radiation pattern using tools from statistics and machine learning. We propose a new approach that combines a deep analytic understanding of jet substructure with the optimality promised by machine learning and statistics. After specifying an approximation to the full emission phase space, we show how to construct the optimal observable for a given classification task. This procedure is demonstrated for the case of quark and gluons jets, where we show how to systematically capture sub-eikonal corrections in the splitting functions, and prove that linear combinations of weighted multiplicity is the optimal observable. In addition to providing a new and powerful framework for systematically improving jet substructure observables, we demonstrate the performance of several quark versus gluon jet tagging observables in parton-level Monte Carlo simulations, and find that they perform at or near the level of a deep neural network classifier. Combined with the rapid recent progress in the development of higher order parton showers, we believe that our approach provides a basis for systematically exploiting subleading effects in jet substructure analyses at the Large Hadron Collider (LHC) and beyond.