A framework and implementation for data-driven trigger efficiency estimation at LHCb

TL;DR

This paper introduces a data-driven framework and software package, TriggerCalib, for estimating trigger efficiencies at LHCb, including uncertainty assessments, to improve particle physics analysis accuracy.

Contribution

It presents a novel, centralized software tool for trigger efficiency estimation based on reconstructed candidate properties, enhancing analysis precision.

Findings

TriggerCalib provides accurate efficiency estimates.

The framework effectively quantifies statistical uncertainties.

Systematic uncertainties are also addressed.

Abstract

Estimations of trigger efficiencies are essential to modern particle physics analyses. A data-driven method provides a framework in which to estimate these efficiencies from the properties of reconstructed candidates, described in this paper. This paper also presents the design, implementation and performance of a software package, TriggerCalib, which provides a first centralised implementation of these calculations and can be seamlessly employed in physics analyses. Additionally, the estimation of statistical and systematic uncertainties is discussed.