Prospects for computer-assisted data quality monitoring at the CMS pixel detector

Data quality monitoring (DQM) and data certification (DC) are of vital importance to advanced detectors such as CMS, and are key ingredients in assuring the correctness of results in high-level physics analyses. The current approach for DQM and DC at CMS is mainly based on manual monitoring of reference histograms which summarize the status and performance of the detector. This requires a large amount of person power, despite the coarse time granularity needed to keep the number of histograms to check manageable. We investigate methods for computer-assisted DQM and DC at the CMS detector, focusing on a case study in the pixel tracker. In particular, using data taken in 2017, we show that autoencoder techniques are able to accurately spot anomalous detector behaviour, with a time granularity previously inaccessible to the human certification procedure.