Data Quality Check Procedures and Tools for Monte

Data Quality Check Procedures and Tools for Monte Carlo Production Eric van Herwijnen Miriam Gandelman September 17 th 2003

Contents u u u u Objectives Quality Check Procedures CMT packages Reference Data (Boole, Brunel, Gauss, SICBMC) Tools Integration into DIRAC Conclusions

Objectives u Procedure for checking MC data n n n u Tools for local managers to check remotely Check log files for small nb of important quantities Integrate into DIRAC Tools for sw managers to check consistency between different versions n Tools to analyze histograms of large samples (50 k)

Quality Check Procedures 1. New versions of Boole, Brunel 1. Quality quantities defined for Trigger, Velo (Boole) and Inner/Outer Tracker and Rich (Brunel) 2. Use reference tables of quantities from previous production 3. Tools to compare quantities from a log file (in production) with the reference values -> quality report web page 4. Tools to compare histograms with reference sets 2. Quality of production data 1. Make new reference tables when data is ok’d by physics coordinator 2. Quality report: Brunel_QA_xxx_yyy. html (xxx production number, yyy jobnumber) 3. Deviations > 3 sigma from reference values are printed in red

CMT packages 1. 2. 3. 4. 5. SICB/SICBMCquality v 1 r 0. Histograms made with v 260 r 1, 2, 3. Quality/Boole v 1 r 0. Tables and histograms made with Brunel v 18 r 1 and Boole v 1 r 0. Quality/Brunel v 1 r 3. Tables and histograms made with Brunel v 18 r 1 and v 20 r 0 p 1. Quality/Tools v 1 r 1. Python (analyse log files), c++ (root for histograms) Reference histograms are stored also at: http: //lhcb-wdqa. web. cern. ch/lhcb-wdqa/vol 11/packagename/packageversion/evttype/index. html

Reference Data (Boole) u u Boole v 1 r 0 Tables: n n u Trigger L 0 Acceptance, L 1 Efficiency Reference sets made with Brunel v 18 r 1 Histograms: n n Velo (MCVelo. Hits, Velo. Clusters), IT (MCITDeposit. Check, MCITDigit. Checker, ITDigit. Checker), Rich (DIGI, occupancies) Histograms made with Boole v 1 r 0

Reference Data (Brunel) u u Brunel v 18 r 1 Tables: n n u Overall tracking efficiency, number of reconstructed Ks, IT efficiencies, Pi/K efficiencies and mis. ID rates Tables made with Brunel v 18 r 1 Histograms: n n ITCluster. Checker, RICH PIDs, OTCluster. Checker, OTCluster. Monitor Histograms made with Brunel v 20 r 0 p 1

Reference Data (Gauss) u u No histograms printed by Gauss Standard Gaudi algorithms could access subdetector information (nb of hits) and generator information

Reference data (SICBMC) u u SICBMC v 260 r 1, 2, 3 Tables: n u Efficiency of acceptance cuts Histograms: n Outer tracker, Velo, Rich, Ecal, Hcal, Muon (nb of hits) and Generator (multiplicities, production/decay points, pileups, primary z vtx)

Tools u Log file analysis tools n n n u Use python 2. 2 Require logfiles to be accessible from bookkeeping database Reference sets are made by hand, comparisons automatically done in production Histogram analysis tools n n Use the wdqa package (add hbook files, convert to root, create gif images, web pages) Use Kolmogorov test for comparisons

Integration into DIRAC u u u Quality tables packaged with Dirac Tools called from scripts via environment variables Output of tools are html pages, put in the same web location as the log file

Conclusions u u Need more experience of tools in production; initial tests in Lyon succesful Need quality information from missing subdetectors Need to write Gaudi algorithms for Gauss tables and histograms More details in note LHCb 2003 -122: http: //lhcb-comp. web. cern. ch/lhcb-comp/dataquality/dqnote. pdf