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 solid results of high-level physics analyses. The current approach for DQM and DC at CMS is mainly based on manual monitoring of reference histograms summarizing the status and performance of the detector. This requires a large amount of person power while having a rather coarse time granularity 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.