CMS Pixel Data Quality Monitoring Petra Merkel Purdue University, West Lafayette, IN, USA For the CMS Pixel Group Interactive SVG Maps The Data Quality Monitoring (DQM) system in the CMS experiment has been developed within the CMS Software framework. It has been designed to be used during online data taking as well as during offline reconstruction. The goal of the online system is to minimize data loss due to wrong detector configurations and malfunctioning components. On the other hand the reconstruction or calibration problems can be detected during offline processing using the same tools. All histograms can be displayed both in the central CMS DQM graphical user interface (GUI), as well as in the Pixel specific expert GUI and in geometrical, interactive maps. Investigate with expert GUI Module with most entries  noisy pixel(s)? Expert DQM GUI Navigate detector Hierarchy to pin point Noisy module The monitoring is performed with histograms, which are filled with information from raw and reconstructed data, summary and overview plots are created, and automated statistical tests are applied. In particular we monitor readout errors, raw charge deposition information, as well as reconstructed hits, both on and off tracks. Thus, we are able to detect data corruption, mis-configuration and mis-calibration of the detector, as well as newly broken modules and dead or noisy pixels. Mean cluster charge [ke-] Endcap modules Clusters: gain calibrated, clustered charges Occupancy map Digis: raw single pixel charges LIVE Plot noisy module’s Raw charge Barrel modules Mean number of Rechits Rechits: Lorentz drift corrected reconstructed hits Data corruption & Readout errors Error map Check which pixels fired A handful of noisy pixels! Reduced statistics (~5Hz), full granularity, Fast feed back to detector Feed back to detector: e.g. mask noisy pixels Fill and investigate online plots, Automatically & by expert & 1440 modules 66M pixels No errors Feed back to detector, calibration and reco groups Offline shifter Fill and investigate offline plots, Automatically & by expert Full calibration, alignment, reconstruction & statistics Reduced granularity Trend monitoring Run number OFFLINE Data Certification Algorithms & cuts Pixel Barrel and Endcap Good run flags for Physics Analyses DQM good run flag (fraction of modules passing cuts) Data base DAQ good run flag (fraction of pixel modules in read out) DCS good run flag (fraction of pixel modules powered up) In order to certify the data for Physics analyses a complex workflow has been put in place. Within the DQM framework a multitude of quantities concerning the data from the Pixel detector are filled into histograms for various steps in time, ranging from a few minutes, over once per run to trend plots spanning weeks or even months. Some of these histograms are automatically being compared to reference histograms, while from others, like the raw charge or reconstructed hit occupancy we extract the average values and apply cuts to them in order to automatically spot outliers and unexpected behaviour. The results of this evaluation are then combined to a final Data Quality Flag, which is stored for each run into a data base.