Anomaly Detection for the ATLAS Pixel Detector

<!--HTML-->Since the ATLAS Detector is exposed to an intense environment during Run-3 and additionally due to its age, the operation of the detector becomes even more challenging. These challenges introduce difficulties in ensuring high data quality standards. In order to counteract against that, identifying the emerging problems in the Data Acquisition (DAQ) and Detector Control System (DCS) plays a crucial role. Therefore, a Machine Learning based anomaly detection method is employed. This method detects outliers of various time series data coming from the DAQ and DCS, to identify emerging problems before they impact the data quality. This talk will present first results of feasibility studies of using such methods in the ATLAS Pixel Detector as an example use case.