LHCb Topological Trigger Reoptimization

21st International Conference on Computing in High Energy and Nuclear Physics

物理学;计算机科学

Likhomanenko, Tatiana^1,2,3 ; Ilten, Philip^5 ; Khairullin, Egor^1,4 ; Rogozhnikov, Alex^1,2 ; Ustyuzhanin, Andrey^1,2,3,4 ; Williams, Michael^5

Yandex School of Data Analysis (YSDA), Russia^1

National Research University Higher, School of Economics (HSE), Russia^2

NRC Kurchatov Institute, Russia^3

Moscow Institute of Physics and Technology, Moscow, Russia^4

Massachusetts Institute of Technology, United States^5

关键词: B-hadron decays;    Blending techniques;    Boosted decision trees;    Classification algorithm;    Proton proton collisions;    Reoptimization;    Systematic uncertainties;    Trigger algorithms;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/664/8/082025/pdf
DOI  :  10.1088/1742-6596/664/8/082025

学科分类：计算机科学（综合）

来源: IOP

【 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.