LHCb Topological Trigger Reoptimization

TL;DR

This paper details the reoptimization of the LHCb topological trigger for Run 2, employing advanced machine learning techniques to enhance b-hadron decay selection efficiency and reduce systematic uncertainties.

Contribution

It introduces new machine learning optimization methods and boosting techniques to improve trigger performance for diverse b-hadron decays in LHCb Run 2.

Findings

Significant expected performance improvement over Run 1

Effective use of ensemble and boosting methods

Reduced systematic uncertainties in measurements

Abstract

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.