Post on 01-Jan-2016
GRAPES-3 ROOT Framework
Pravata K Mohanty
Tata Institute of Fundamental ResearchOn behalf of the GRAPES-3 collaboration
Workshop on Astroparticle Physics, Bose Institute, Darjeeling,10 - 12 December 2009
Scintillator Detectors (ADC+TDC)
400 + ………721
Proportional Counters (muon hit+ pulse width)
3712 + ………7424
Scint. Count Rate
Monitoring
DAQ(1GB/day)
EASDAQ
(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
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 Orie
nted Approach
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.
Our ApproachStep 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.
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
ScEventTree
EventHeader ScDATA
runno
eventno
trigger
evdate
evtime1
evtime2
evcurdtime
evabsdtime
ndet
detno
adchh
adchl
adclh
adcll
tdc
tdctype
Tree structure for scintillator data
Root[0] scevtree->Draw(“tdc>>h1(4095,0,4095)”, ”detno==1 && trigger==2&& evtime>010000 && evtime <=020000”)
Interactive Debugging
Self Trigger
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
Detectors calibrated by generating muon trigger using two paddles placed below scintillator
Several detectors calibrated in dayby moving paddles to different detectors
Algorithm developed to identify calibrated detectors in the data
Muon data analysed to get calibratedparameters and stored into thedatabase
Scintillator Calibration with Muons
Diagnostic Parameters for good and Bad Detector
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.
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.
THANKS
GRAPES-3 Data Analysis Architecture
ROOT DATA
SC RAW DATA
Monitoring
Shower Reconstruction
Shower Reconstruction
GEANT4
CORSIKAShower 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
GRAPES-3 Scintillator Data Structure
RUN
Event Event Event Event Event
Trigger ADC TDCTime
Det1
Det2
Det1
Det2
~8000/RUN
~ 350/day
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.
GRAPES-3 analysis code summary ● The code consists of
– 25 classes
– 30 000 lines
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
Signal Rate
P.I
. (0-
100%
)