Speaker
Description
The ALICE (A Large Ion Collider Experiment) is a general-purpose heavy-ion detector at the CERN Large Hadron Collider (LHC) that operates at interaction rates producing raw data streams of O(TB/s). Due to these data volumes, an online reconstruction is performed to achieve a compressed representation of the continuous data stream. Given the lossy nature of this process, early assessment of data quality and processing is critical. To address this challenge, the online Data Quality Monitoring (DQM) serves as a first line of defense against detector malfunctions and data corruption. This is performed through a combination of rule-based checks and continuous 24/7 visual inspection of monitoring objects, such as histograms and trends, by operators. This approach is subject to several limitations such as the operational cost associated with continuous human shift coverage, inherent human-factor constraints, and the increasing difficulty of defining checks under evolving detector configurations and running conditions. In this work, we explore semi-supervised and unsupervised machine learning techniques, including representation learning and embedding-based approaches, for anomaly detection in online DQM data from key ALICE sub-detectors. The goal is to reduce anomaly detection latency and enable automation of routine quality control tasks currently performed by operators. Preliminary results indicate that automated anomaly detection can be achieved while maintaining a low false discovery rate, demonstrating the potential of these approaches to support and enhance DQM.