A software framework for Data Quality Monitoring in ATLAS

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 CHEP 07 Conference

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

3. Muon Spectrometer Inner Detector Calorimeter TGC Pixel SCT TRT TileCal LAr MDT CSC RPC 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 105 • The Data Quality Challenge: • Fast and reliable detection of DQ problems during data taking and reconstruction CHEP 07 Conference

4. Muon Spectrometer Inner Detector Calorimeter DQM Input TGC Pixel SCT TRT TileCal LAr MDT CSC RPC 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 DQ Configuration • DQ Result: • {Red, Yellow, Green} • Optional Tags • TObject DQM Config Histograms, Graphs, Rates, etc. DQM Core DQM Output CHEP 07 Conference

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

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

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

11. Node B Node C DQMF Agent DQMF Agent DQMF Agent On-line DQMF Scalability Node A • 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 Calo Muon Tile Calo Liquid Argon Calo 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++ CHEP 07 Conference

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

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

16. Backup CHEP 07 Conference

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