1 Checks on SDD Data Piergiorgio Cerello, Francesco Prino, Melinda Siciliano

2 GoalsGoals Checks on the data at different levels Controls from the acquisition to the reconstruction of data Controls of the calibrations

3 OutlineOutline AMORE online monitoring AMORE online monitoring Raw data monitoring RecPoint Monitoring CDB Monitoring Calibration Monitoring Calibration Monitoring Offline QA and Fast Checks at Point2 Offline QA and Fast Checks at Point2 Raw Data QA RecPoint QA Trend QA QA Train QA Train

4 AMORE DB DAQ SDD Amore Agent AMORE CDB AliRootQA SDD GUI ALICE SDD Data Quality Monitoring Two lists filled: Raw Data Clusters Extra: Calibration Monitoring Extension by a flag of the QA histogram list managed by AMORE to add more information about the detector and acquisition status during the data taking

5 SDD DQM Distributions Raw Patterns Hit Maps single event DDL connections Data Size Raw Data distributions

6 SDD DQM Distributions DQM histograms RecPoints Patterns Local Coordinates Global Distribution FSE RecPoints Distributions

7 SDD Online DA Monitoring DA: send histograms to AMORE DataBase Three distributions for each quantity: – Parameter distribution – Difference distribution – Trend distribution

8 Four macros for calibration parameters checks Stored in $ALICE_ROOT/ITS/macrosSDD ShowCalibrationSDD.C: Module Status of the SDD starting from the output of the PEDESTAL and PULSER calibration run stored in the OCDB (OCDB/ITS/CalibSDD) ShowDriftSpeedSDD.C: Drift Speed and Injector Status from the output of a INJECTOR run stored in the OCDB (OCDB/ITS/DriftSpeedSDD) SDD Offline Calibration Checks

9 Trend macros of the calibration parameters PlotCalibSDDVsTime.C: Trend of the good anodes in acquisition from PEDESTAL and PULSER runs PlotDriftSpeedVsTime.C : Trends of the modules Drift Speed from the INJECTOR Runs SDD Offline Calibration Checks

10 Some Plots Fraction of Good Anodes Noise Drift Speed

11 SDD QA Offline Raw Data: – Pattern Distributions – Normalized Pattern to number of good Anodes and number of Events RecPoints: – Charge – Drift time – Local and Global Coordinate Distribution – Radius and angular distribution – Patterns and Normalized Patterns

12 SDD QA Offline Different checks – Check on single chunk (non (yet) reconstructed data) – Check on single run (reconstructed data) – Trend: Check on more runs (more analyzed data) Data are so checked in different phases Comparison between different data takings

13 After the data migration Fast checks on the collected data by mean of the QA distribution on a data sample Tested on during the last week data taking Kit: – QA execution macro: soon in the trunk – Visualization macro: in the trunk Check on a single chunk

14 Example: Run One chunk Integrated Global Distributions Charge Drift Time

15 After reconstruction pass Checks on the reconstructed data by mean of the QA distribution on a single run or a groups of chunks Available on the trunk Visualization kit: – Script: ShowSDDQA.sh Ask the relevant information for the execution of the two macros (run number, period, pass, year) Creation of the folders that files and images are stored – Merging macro: ReadQASDD.C Query of the Merged.QA.data.root files present in the chunk and merging in on file called File.QA.year.period.pass.Run.run.root – Visualization macro: PlotSDDQA.C – Take the the SDD histograms of the QA file and plot them in the canvas – Creation of ps file that collects the images of the plot is created and optionally eps files of the images. Check on a single run

16 Example: run LHC10g

17 QA Trend Check the values of some relevant quantities in function of the run number in a certain range All in the trunk Visualization kit: – TrendingSDD.sh: Script that guides the user to introduce the relevant information needed to the visualization Charge trend Normalized Charge and drift time superposition plots – TrendSDDQA.C : Macro that creates the trend plots, save them in a ps file and store them in a root file

18 Example: Trend LHC10h Last 24h Charge Trend Normalized Charge Superposition Normalized Drift Time superposition

19 QA train SDD task running on QA train, to check From RecPoints  Detector occupancy  Drift time distribution From ESD:  SDD points in tracks (good, missing, track crossing dead region)  dE/dx information from SDD (vs. module number) From TrackPoints (= clusters associated to tracks)  Detector occupancy  Drift time distribution, extra clusters  dE/dx vs. drift time  Cluster size Macro to plot the results for single run/chunk on svn and used by SDD experts on call and shifter to check the detector performance Macro to plot trending vs. run number prototyped,first version on svn, new developments ongoing. QA Train

20 ESD plots From run pass1plus Pseudo-efficiency for each module estimated from: - number of tracks with point in SDD - number of tracks crossing dead region -number of tracks with missing point in SDD ESDs Plots

21 Track Points Plots

22 Conclusions Good performance of the SDDs !!!! Differents data checks at different levels From data taking to the ESDs Calibration checked at different levels too Most of the macros are in $ALICE_ROOT/ITS/macrosSDD All the macros have been tested with success!

23 Thanks!!

24 Example: Trend LHC10h Last 24h

25 Example: Trend LHC10h Last 24h

26 Example: LHC10h Last 24h

27 Basic Idea: exploit the possibility to create and fill by mean the Offline Quality Assurance framework the relevant distributions and publish them to the AMORE database, the ALICE framework for the Online Data Quality Monitoring. Extension by a flag of the QA histogram list managed by AMORE to add more information about the detector and acquisition status during the data taking. Two lists filled: Raw Data Clusters Calibration Monitoring AMORE Online Monitoring