Speaker
Description
The CMS experiment at CERN relies on Data Quality Monitoring (DQM) and data certification to ensure that only high-quality data are used for physics analyses. For the JetMET subsystem, this process is traditionally based on the manual inspection of a large number of DQM histograms by detector experts, making it a time-consuming task that can make subtle detector or reconstruction issues difficult to identify consistently. To support the JetMET offline certification workflow, we first explored unsupervised anomaly detection using autoencoders to identify runs with anomalous detector or reconstruction behaviour from DQM histograms. Building on this work, we integrated machine learning models into the Data Inspector for Anomalous Lumi-Sections (DIALS), a framework providing access to per-lumisection DQM information, and developed a workflow to produce Machine Learning JSON (MLJSON) certification files containing the run and lumisection selections for physics analyses. We also developed a web application to simplify model training, validation, and result visualization for JetMET experts. This contribution presents the machine learning developments introduced for JetMET during Run 3, their integration into the CMS certification workflow, and the ongoing work towards preparing these tools for the High-Luminosity LHC era.