1. September 2007CHEP 07 Conference 1 A software framework for Data Quality Monitoring in ATLAS S.Kolos, A.Corso-Radu University of California, Irvine, M.Hauschild CERN, B.Kehoe, H.Hadavand Southern Methodist University

2. September 2007 CHEP 07 Conference 2 Outline The ATLAS Data Quality challenge Data Quality Framework –Design & Implementation –Experience during ATLAS commissioning Conclusion

3. September 2007 CHEP 07 Conference 3 ATLAS DQ Challenge 3 main detectors 10 sub-detectors + LVL1 Trigger 33 sub-systems Each sub-system can be used independently 140M channels Bunch crossing rate 40MHz 3 Trigger levels reducing rate by 10 5 PixelTileCalLArMDTCSCSCTTRT Calorimeter Inner Detector Muon Spectrometer RPC TGC The Data Quality Challenge: –Fast and reliable detection of DQ problems during data taking and reconstruction

4. September 2007 CHEP 07 Conference 4 The Model of Data Quality Framework DQM Core: –Read DQ Configuration –Algorithm execution is data-driven: Execute DQ algorithms as soon as input data becomes available –Produce DQ Result DQM Core DQM Input DQM Output DQM Config Histograms, Graphs, Rates, etc. DQ Result: {Red, Yellow, Green} Optional Tags TObject DQ Configuration PixelTileCalLArMDTCSCSCTTRT Calorimeter Inner Detector Muon Spectrometer RPC TGC

5. September 2007 CHEP 07 Conference 5 DQ Configuration PixelTileCalLArMDTCSCSCTTRT Calorimeter Inner Detector Muon Spectrometer RPC TGC Atlas Inner Detector CaloMuon Tile Calo Weighted Summary Liquid Argon Calo Worst Result Summary Module N NumOfEvents Histogram NOT Empty Module 1 NumOfEvents Histogram NOT Empty Energy GausFit EtaPhi Chi2 DQ Region: Produces summary result for given sub-system It is based on the children results DQ Parameter: Produces DQ result for a given quantity Analyses input data with specific algorithm

6. September 2007 CHEP 07 Conference 6 DQ Algorithm C++ function which analyses histogram and produce the DQ Result for the corresponding DQ Parameter Special type of DQ Algorithms is SummaryMaker: –Produces DQ Result for a DQ Region –This result is based on the DQ Results of the Region’s children Custom algorithms can be easily plugged in by: –Inheriting from the Algorithm base class –Implementing two abstract methods: clone and execute

7. September 2007 CHEP 07 Conference 7 Data Quality Algorithms DQMF provides shared algorithms repository: –contains a set of predefined algorithms: Reference Histogram comparison –Chi2, Kolmogorov, Bin comparison (using ROOT) Fits: –Gaus, Landau, Pol1 (using ROOT) Bin Threshold:, = Basic Histogram checks –All_Bins_Filled, Histogram_Not_Empty, No_UnderFlows, No_OverFlows Basic Stat Checks –CheckHisto_Mean, CheckHisto_RMS Etc. –Contains 2 summary makers: Worst result Weighted result Repository is filled up by ATLAS sub-system experts: –Share experience between sub-systems –Avoid duplication of work

8. September 2007 CHEP 07 Conference 8 DQ Algorithm Data Flow DQ Algorithm Input Data (Histogram) DQ Result DQ Configuration Parameters Thresholds References Names (several references for different conditions) Reference Histograms 1 2 3 4

9. September 2007 CHEP 07 Conference 9 DQM Workbench DQMF provides set of Root macros which can be used to run DQ Algorithms in Root shell: –For algorithms development and debugging –For preparing configuration root [0].x Workbench.C root [1].x TestBinContentCompAlgorithm.C Number of bins 2 Sigma away from reference is 16 Green threshold: 33 bin(s); Red threshold : 1 bin(s) Result 2

10. September 2007 CHEP 07 Conference 10 On-line Data Taking Off-line Reconstruction On-line vs. Off-line DQMF is used for DQ assessment during the online data taking as well as during the off-line reconstruction DQM Core DQM Input DQM Output DQM Config Online Histogramming Service Online Information Service Online Configuration Service ROOT File Conditions DB ROOT File

11. September 2007 CHEP 07 Conference 11 On-line DQMF Scalability Online environment implies high performance requirements for the DQ Assessment The DQMF functionality is provided by a process called DQMF Agent –Any DQRegion can be processed by individual DQMF Agent on a separate node Atlas Inner Detector CaloMuon Tile CaloLiquid Argon Calo Node A DQMF Agent Node B DQMF Agent Node C DQMF Agent

12. September 2007 CHEP 07 Conference 12 DQMF at the ATLAS Commissioning Online DQMF configuration for the last Commissioning run includes ~2000 histograms: –Signals distributions, energy distributions, noise distributions, Trigger rate, etc. –This configuration has been handled by a single DQMF agent process Lessons learned: –The JAIDA which we were using for the DQMF display prototype does not fulfill our performance & scalability requirements –The final version will be implemented using ROOT/Qt/C++

13. September 2007 CHEP 07 Conference 13 Online DQMF Display (1/2)

14. September 2007 CHEP 07 Conference 14 Online DQMF Display (2/2)

15. September 2007 CHEP 07 Conference 15 Summary & Conclusion DQMF is framework for DQ analysis which is: –Efficient – implemented in C++ using ROOT –Flexible - fully configurable via simple interface –Simple – no code development is necessary if standard algorithms can be used –Scalable – can be naturally distributed over any number of machines It is being successfully used now in both On-line and Off-line environments for the ATLAS commissioning

16. September 2007CHEP 07 Conference 16 Backup

17. September 2007 CHEP 07 Conference 17 Software development process The standard software development model has been found extremely useful Requirements collection Design Implementation Discussion At this stage the scope of the framework has been extended to the Off-line reconstruction Design has been influenced by the feedback to the Requirements document C++