1. 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.