Introduction/Welcome

• Thomas Britton (JLab)

ATLAS status and future plans

• Florian Haslbeck (University of Oxford / CERN)

Traditional DQM at JLAB

• Torri Jeske (JLAB)

Siamese twin models

• Kishansingh Rajput (Thomas Jefferson National Accelerator Facility)

Data-Driven Detection, Identification, and Prediction of Accelerator Cavity Faults

• Christopher Tennant (Jefferson Lab)

Machine Learning based Anomaly Detection for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter

• Kyungmin Park (Carnegie Mellon University / CERN)

Online Data Quality Monitoring (DQM) of the CMS electromagnetic calorimeter (ECAL) is a vital operational tool that allows ECAL experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. Although the ECAL DQM system has been in operation since the start of the LHC and continuously updated to respond to new problems, it is challenging to anticipate anomalies in different shapes and sizes that had not been observed before. With the need for a more robust anomaly detection system, a real-time semi-supervised machine learning based method is developed using an autoencoder model. After accounting for spatial and time-dependent deviations in the ECAL response, the autoencoder based online DQM system is able to detect and localize anomalies with an estimated false discovery rate of 10^{-2} to 10^{-4} at 99% anomaly detection rate. We present anomaly detection results from the ECAL Barrel and Endcap regions, including the deployment results with early LHC Run3 collision data.

Hydra: Computer Vision for Data Quality Monitoring

• Torri Jeske (JLAB)
• Thomas Britton (JLab)