Abstract

The CMS Muon System has been operated successfully during the two LHC runs allowing to collect a very high fraction of data with a quality that fulfils the requirements to be used for physics analysis. Nevertheless, the workflows used nowadays to operate and monitor the detector are rather expensive in terms of human resources. Focus is therefore being put on improving such workflows, both by applying automated statistical tests and exploiting modern machine learning algorithms, in view of the future LHC runs. The ecosystem of tools presently in use will be presented, together with the state of the art of the developments toward more automatized monitoring and the roadmap for the future.

Highlights

  • 1.1 The CMS experiment and its muon systemThe Compact Muon Solenoid (CMS) experiment [1] is a general purpose particle physics detector operating at the CERN Large Hadron Collider (LHC) [2]

  • In CMS, muons are key ingredients and are measured with detection planes instrumented with four detector technologies: drift tubes (DT), cathode strip chambers (CSC), resistive plate chambers (RPC), and recently with gas electron multipliers (GEM) [6] for enhancing the forward region trigger and reconstrution [7]

  • The CMS Muon community is actively pursuing a variety of modern solutions to push the Data Quality Monitoring (DQM) process towards higher levels of automation

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Summary

The CMS experiment and its muon system

The Compact Muon Solenoid (CMS) experiment [1] is a general purpose particle physics detector operating at the CERN Large Hadron Collider (LHC) [2]. Data collected with the CMS detector have been used to produce many excellent scientific results in modern particle physics, for instance the discovery [3] and characterization[4] of the Standard Model Higgs boson. In CMS, muons are key ingredients and are measured with detection planes instrumented with four detector technologies: drift tubes (DT), cathode strip chambers (CSC), resistive plate chambers (RPC), and recently with gas electron multipliers (GEM) [6] for enhancing the forward region trigger and reconstrution [7]. A detailed description of the CMS muon detectors and their performance can be found in [5]

The CMS data quality monitoring
Current limitations and future automation
An automated statistical approach
Neural networks for detecting DT anomalies
CMS L1 Muon trigger monitoring with ML
Findings
Conclusions and outlook
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