标题： LHCb 顶点触发重新优化 显示英文标题

标题： LHCb Topological Trigger Reoptimization

摘要： 由LHCb实验使用的主b物理触发算法是所谓的拓扑触发。 拓扑触发选择的顶点满足a) 与主要质子-质子碰撞分离，以及b) 与来自b介子衰变相容。 在LHC运行1期间，该触发器利用了一个定制的提升决策树算法，选择了几乎100%纯的b介子样本，典型效率为60-70%；其输出被用于大约60%的LHCb论文。 本次报告介绍了为优化LHC运行2的拓扑触发而进行的研究。 特别是，我们对各种机器学习分类器算法进行了详细比较，例如， AdaBoost、MatrixNet和神经网络。 拓扑触发算法旨在选择所有“有趣”的b介子衰变，但不能对每种这样的衰变进行训练。 因此，已经进行了研究以确定如何优化分类算法在未用于训练的衰变上的性能。 研究的方法包括级联、集成和混合技术。 此外，已经实现了新的提升技术，这将有助于减少运行2中的系统不确定性。 我们证明，重新优化的拓扑触发预计将在广泛的b介子衰变中显著优于运行1的性能。 显示英文摘要

摘要： The main b-physics trigger algorithm used by the LHCb experiment is the so-called topological trigger. The topological trigger selects vertices which are a) detached from the primary proton-proton collision and b) compatible with coming from the decay of a b-hadron. In the LHC Run 1, this trigger, which utilized a custom boosted decision tree algorithm, selected a nearly 100% pure sample of b-hadrons with a typical efficiency of 60-70%; its output was used in about 60% of LHCb papers. This talk presents studies carried out to optimize the topological trigger for LHC Run 2. In particular, we have carried out a detailed comparison of various machine learning classifier algorithms, e.g., AdaBoost, MatrixNet and neural networks. The topological trigger algorithm is designed to select all "interesting" decays of b-hadrons, but cannot be trained on every such decay. Studies have therefore been performed to determine how to optimize the performance of the classification algorithm on decays not used in the training. Methods studied include cascading, ensembling and blending techniques. Furthermore, novel boosting techniques have been implemented that will help reduce systematic uncertainties in Run 2 measurements. We demonstrate that the reoptimized topological trigger is expected to significantly improve on the Run 1 performance for a wide range of b-hadron decays.