LHCb Topological Trigger Reoptimization
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Likhomanenko, Tatiana; Khairullin, Egor; Rogozhnikov, Alex; Ustyuzhanin, Andrey; Ilten, Philip J; Williams, Michael; ... Show more Show less
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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.
Date issued
2015-12Department
Massachusetts Institute of Technology. Department of PhysicsJournal
Journal of Physics: Conference Series
Publisher
IOP Publishing
Citation
Likhomanenko, Tatiana; Ilten, Philip; Khairullin, Egor; Rogozhnikov, Alex; Ustyuzhanin, Andrey and Williams, Michael. “LHCb Topological Trigger Reoptimization.” Journal of Physics: Conference Series 664, no. 8 (December 2015): 082025. © 2015 IOP Publishing
Version: Final published version
ISSN
1742-6588
1742-6596