Speaker
Description
Data Quality Monitoring (DQM) is an important process of collecting high quality data for physics analysis. Currently, the workflow of DQM is manpower intensive to scrutinize and certify hundreds of histograms. Identifying good quality and reliable data is necessary to make accurate predictions, simulations, therefore anomalies in the detector must be timely identified to minimize data loss. With the use of Machine Learning (ML) algorithms raising alarms at the anomalies or failures can be automated and data certification process be made more efficient. The Tracker DQM team at the CMS Experiment (at the LHC) has been working on designing and implementing ML features to monitor this complex detector. This contribution presents the recent progress in this direction.
| Are you are a member of the APS Division of Particles and Fields? | No |
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