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GRAPES-3 ROOT Framework

Pravata K Mohanty Tata Institute of Fundamental Research On behalf of the GRAPES-3 collaboration. GRAPES-3 ROOT Framework. Workshop on Astroparticle Physics, Bose Institute, Darjeeling, 10 - 12 December 2009. Scintillator Detectors (ADC+TDC) 400 + ………721. Proportional Counters

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GRAPES-3 ROOT Framework

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  1. Pravata K Mohanty Tata Institute of Fundamental Research On behalf of the GRAPES-3 collaboration GRAPES-3 ROOT Framework Workshop on Astroparticle Physics, Bose Institute, Darjeeling, 10 - 12 December 2009

  2. Scintillator Detectors (ADC+TDC) 400 + ………721 Proportional Counters (muon hit+ pulse width) 3712 + ………7424 Scint. Count Rate Monitoring DAQ (1GB/day) EAS DAQ (8GB/day) PC Count Rate monitoring + muon angle DAQ (5GB/day) GRAPES-3 Data (14GB/day) GRAPES-3 DATA With expanded array the data size ~ 40GB/day or 15 Tera Bytes/year

  3. Mandate • Large storage space and computing power • Efficient monitoring of detectors • Ease of accessing data • Parallel approach to develop data analysis, detector monitoring software with participation of bigger team • Portability of data to wider collaboration Object Oriented Approach

  4. Object Oriented Approach to GRAPES-3 Data • Adaptation of object oriented language C++ • Object oriented design of all analysis programs in form of classes under ROOT framework. • Storage of event data in ROOT Tree structure which provides efficient access of data for analysis. • ROOT provides excellent graphical connectivity to the data object The biggest advantage of OO design is, ease in managing large codes and lot of scope for any future developments. Not so easy in a procedural oriented approach.

  5. Our Approach Step 1: Conversion of binary data to ROOT for various data streams like scintillator and muon detector data for EAS, scintillator rate monitoring data, muon monitoring data and weather data. Step 2: Various monitoring plots to monitor the scintillator and muon detectors using these ROOT files. Built intelligence in the program so that program should pick out the abnormality behavior of the detectors. Step 3: Make a table for abnormality based on monitoring output and the calibration constants in more automated way. Step4: Use this table and the root data to reconstruct various shower parameters like core location (Xc, Yc), arrival direction (, ), shower size Ne, shower age S, number of muons N. CORSIKA + GEANT simulation to convert Ne to E. Store them in ROOT tree. Huge amount of programming effort required to reach step 4.

  6. root [2] scevtree->Show(0)======> EVENT:0 runno = 13528 eventno = 1430 trigger = 2 evdate = 20050201 evtime1 = 0 evtime2 = 32140000 evstatus = 1 ndet = 33 detno = 13, 29, 44, 49, 51, 58, 73, 86, 94, 107, 124, 134, 141, 149, 151, 153, 170, 180, 193, 237 adchh = 1590, 226, 219, 290, 323, 528, 814, 512, 512, 604, 575, 477, 772, 640, 771, 812, 458, 1487, 566, 669 adchl = 213, 31, 32, 41, 46, 71, 108, 67, 68, 78, 74, 61, 101, 83, 101, 106, 59, 196, 76, 90 adclh = -1, -1, 328, -1, -1, 205, -1, -1, 156, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 adcll = -1, -1, 40, -1, -1, 24, -1, -1, 17, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 tdc = 1034, 4095, 960, 4095, 4095, 4095, 832, 4095, 4095, 4095, 4095, 4095, 879, 826, 1069, 959, 4095, 4095, 4095, 4095

  7. Tree structure for scintillator data ScEventTree EventHeader ScDATA runno ndet eventno detno trigger adchh evdate adchl evtime1 adclh evtime2 adcll evcurdtime tdc evabsdtime tdctype

  8. Interactive Debugging Root[0] scevtree->Draw(“tdc>>h1(4095,0,4095)”, ”detno==1 && trigger==2&& evtime>010000 && evtime <=020000”) Self Trigger

  9. Monitoring Tools • Automated Analysis of Scintillator Detector Calibration data with Muons • Remote Monitoring of each Scintillator detector in the array • Performance of detector and DAQ can be monitored by any of the GRAPES-3 collaborator on daily basis • Remote Monitoring of Muon Detector

  10. Scintillator Calibration with Muons Detectors calibrated by generating muon trigger using two paddles placed below scintillator Several detectors calibrated in day by moving paddles to different detectors Algorithm developed to identify calibrated detectors in the data Muon data analysed to get calibrated parameters and stored into the database

  11. Diagnostic Parameters for good and Bad Detector

  12. Remote Monitoring • The monitoring plots and logs uploaded to a common gmail account on daily basis. The remote shifter check the plots, enters the detail of the problem and his feed backs to an excel file and sends back. The feedbacks very useful to take necessary action taken by the people at the experimental site. • Remote Shifters at present Supriya Das, Sumana Das (Bose Institute, Kolkata), Sonali Bhatnagar (Dalbag Institute, Agra), S.R. Dugad, S.K.Gupta, P.K. Nayak, P.K. Mohanty, S.D. Morris (Mumbai) We are expecting more participation in this activity from the collaborating institutes.

  13. Summary • The Object Oriented design of GRAPES-3 data analysis software is robust and efficient • GRAPES-3 ROOT framework is a team effort. • ROOT framework implemented for scintillator and muon data • Reconstruction program ready in ROOT framework • Plans for online reconstruction, online alert for solar activity. • Still many more things to be developed. Needs involvement of more people.

  14. THANKS

  15. GRAPES-3 Data Analysis Architecture ROOT DATA SC RAW DATA Monitoring Shower Reconstruction Shower Reconstruction GEANT4 CORSIKA Shower Parameters in ROOT (Xc,Yc, Ne, S, , , E, N) Ne – E Relation Calibrations + Bad data summary MU RAW DATA ROOT DATA Event Matching Muon Reconstruction -ray astronomy Energy spectrum and composition

  16. GRAPES-3 Scintillator Data Structure Trigger RUN Event Event Event Event Event TDC Time Det1 Det2 Det1 Det2 ADC ~ 350/day ~8000/RUN

  17. Shower Reconstruction Arrival direction (,) reconstruction using plane fit and cone fit Shower size Ne, Age S and Core location (Xc,Yc) by fitting NKG function using log likely hood method ROOT TMinuit class for minimization CORSIKA for shower simulation and GEANT4 for detector simulation.

  18. GRAPES-3 analysis code summary The code consists of 25 classes 30 000 lines

  19. Scintillation Array Monitor Detector Monitor Parameters Noise: Pedestal Mean/RMS Uniformity of Analog Data: Signal Rate, Mean and RMS Signal Timing (TDC): Rate, Mean, RMS Determine all 8 parameters for each run in a day Obtain performance index (PI) of each parameter P.I. (0-100%) Signal Rate

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