Performance of the CMS high-level trigger during LHC Run 2

TL;DR

This paper evaluates the performance of the CMS high-level trigger system during LHC Run 2, highlighting the deployment of advanced algorithms like machine learning for real-time data selection in high-energy physics experiments.

Contribution

It presents the implementation and performance assessment of sophisticated online algorithms, including machine learning techniques, for the CMS high-level trigger during LHC Run 2.

Findings

Effective online deployment of offline-like algorithms.

Successful use of machine learning for b tagging.

Robust trigger performance under high luminosity conditions.

Abstract

The CERN LHC provided proton and heavy ion collisions during its Run 2 operation period from 2015 to 2018. Proton-proton collisions reached a peak instantaneous luminosity of 2.1 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$, twice the initial design value, at $\sqrt{s}$ = 13 TeV. The CMS experiment records a subset of the collisions for further processing as part of its online selection of data for physics analyses, using a two-level trigger system: the Level-1 trigger, implemented in custom-designed electronics, and the high-level trigger, a streamlined version of the offline reconstruction software running on a large computer farm. This paper presents the performance of the CMS high-level trigger system during LHC Run 2 for physics objects, such as leptons, jets, and missing transverse momentum, which meet the broad needs of the CMS physics program and the challenge of the evolving LHC and detector conditions. Sophisticated algorithms that were originally used in offline reconstruction were deployed online. Highlights include a machine-learning b tagging algorithm and a reconstruction algorithm for tau leptons that decay hadronically.