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