Performance of the CMS high-level trigger during LHC Run 2
File(s)Hayrapetyan_2024_J._Inst._19_P11021.pdf (4.26 MB)
Published version
Author(s)
Type
Journal Article
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× 1034 cm-2s-1, twice the initial design value, at √(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.
Date Issued
2024-11
Date Acceptance
2024-11-03
Citation
Journal of Instrumentation, 2024, 19 (11)
ISSN
1748-0221
Publisher
IOP Publishing
Journal / Book Title
Journal of Instrumentation
Volume
19
Issue
11
Copyright Statement
© 2024 CERN for the benefit of the CMS collaboration. Published by
IOP Publishing Ltd on behalf of Sissa Medialab. Original content from
this work may be used under the terms of the Creative Commons Attribution 4.0 licence.
Any further distribution of this work must maintain attribution to the author(s) and the
title of the work, journal citation and DOI.
IOP Publishing Ltd on behalf of Sissa Medialab. Original content from
this work may be used under the terms of the Creative Commons Attribution 4.0 licence.
Any further distribution of this work must maintain attribution to the author(s) and the
title of the work, journal citation and DOI.
License URL
Identifier
https://doi.org/10.1088/1748-0221/19/11/p11021
Publication Status
Published
Article Number
P11021
Date Publish Online
2024-11-22