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EXPERIMENTS WITH LARGE GAMMA DETECTOR ARRAYS Lecture III. Ranjan Bhowmik Inter University Accelerator Centre New Delhi -110067. DATA ACQUISITION AND ANALYSIS. MAIN COMPONENTS OF DATA ACQUISITION SYSTEM. Why do we need Advanced Data Acquisition System ?. - PowerPoint PPT Presentation
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EXPERIMENTS WITH LARGE EXPERIMENTS WITH LARGE GAMMA DETECTOR ARRAYSGAMMA DETECTOR ARRAYS
Lecture IIILecture III
Ranjan Bhowmik
Inter University Accelerator Centre
New Delhi -110067
Lecture III SERC-6 School March 13-April 2,2006
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DATA ACQUISITION AND DATA ACQUISITION AND ANALYSISANALYSIS
Lecture III SERC-6 School March 13-April 2,2006
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MAIN COMPONENTS OF DATA MAIN COMPONENTS OF DATA ACQUISITION SYSTEMACQUISITION SYSTEM
Lecture III SERC-6 School March 13-April 2,2006
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Why do we need Advanced Data Why do we need Advanced Data Acquisition System ?Acquisition System ?
Primary objective is to have the most comprehensive information about the physical process under study.
For complex processes, many outgoing channels would require a large number of sensors and the simultaneous collection of data from all the channels.
Typical present generation experiments would have:
• 200 parameters per event• 10,000 events per second• > 1012 bytes of data / experiment.
Needs high throughput and massive storage requirement.
Lecture III SERC-6 School March 13-April 2,2006
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Data Acquisition for Large ArraysData Acquisition for Large Arrays
The front end electronics for a large array has to perform the following tasks:
Generate logic signals indicating the arrival of a particle or photon
Analogue processing of the signal to obtain precise information about energy, time, pulse shape etc
Decide on events of interest i.e. simultaneous arrival of two or more particles or photons
Digitization of all signals associated with an event of interest
Transfer of digitized data to a CPU for further processing & visualization
Lecture III SERC-6 School March 13-April 2,2006
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Increased reliability of operation High degree of integration to minimise the space and
power requirement and reduction of inter-connecting cables
Replacement of manual controls by software control Provision of remote monitoring of data Faster data handling to support the large event rate and
increased number of parameters per event Modular development to allow integration with data
from auxiliary detectors
The Data-Acquisition System should ensure that the capabilities of the front-end can be fully utilized !
SPECIAL REQUIREMENTSSPECIAL REQUIREMENTS
Lecture III SERC-6 School March 13-April 2,2006
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MULTI-PARAMETER ACQUISITIONMULTI-PARAMETER ACQUISITION
Channel selective ‘exclusive’ data collection has been possible due to three parallel developments : better detectors having lower cost per channel advancement in pulse processing increased precision speed in computation analysis of large volume of data.
The limitations of hardware throughput and processing capability have been overcome by
parallel readout of data parallel processing in a distributed network miniaturization of circuit components
saving in space and power requirements.
Lecture III SERC-6 School March 13-April 2,2006
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EUROBALL FACILITYEUROBALL FACILITY 30 Coaxial Detectors 26 segmented Clover
detectors 15 Cluster detectors BGO shields for above Silicon Ball Neutron Array
239 Ge crystals
Efficiency = 0.09
P/T ratio 0.50 • Data throughput 50-
100 KHz 20 Mbytes/sec
Lecture III SERC-6 School March 13-April 2,2006
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VXI-based Front End ElectronicsVXI-based Front End Electronics
Lecture III SERC-6 School March 13-April 2,2006
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Lecture III SERC-6 School March 13-April 2,2006
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Hardware for Indian National Gamma Hardware for Indian National Gamma ArrayArray
The front end electronics for INGA has to provide: 96 Energy signals from the 24 Clover detectors 24 timing signals Anti-Compton logic for each Clover Coincidence logic for Compton suppressed fold Multiplicity logic for unsuppressed gamma fold Gating and pile up rejection for individual channels Electronics for auxiliary detectors like LEPS, recoil
separator, charged particle array, neutron array e.t.c. Synchronization logic to ensure parallel readout from
multiple crates
Lecture III SERC-6 School March 13-April 2,2006
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Clover ElectronicsClover Electronics
Fixed Gain 2/4/6 MeV fs Linearity < 1 in 104
Noise < 100V rms
Gate for valid events PUR for individual channels Total -multiplicity logic
Lecture III SERC-6 School March 13-April 2,2006
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ELECTRONICS FOR INGA ARRAYELECTRONICS FOR INGA ARRAY
Lecture III SERC-6 School March 13-April 2,2006
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DATA ACQUISITION SYSTEM
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DAS PERFORMANCEDAS PERFORMANCE New software CANDLE (Collection and Analysis of
Nuclear Data using Linux nEtwork) developed Up to five CAMAC crates Parallel readout of all crates Readout time < 100 s per event Can handle data rates up to 10 ,000 triggers/sec Fast ADCS ( 10 s ) allowing eight channels/module Online monitoring of singles projection and matrix List mode data contains event by event energy & timing
information from all detectors Compressed data storage in hard disc Data archival in DVD format
Lecture III SERC-6 School March 13-April 2,2006
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DATA ANALYSISDATA ANALYSIS
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ANALYSIS OF LIST MODE DATAANALYSIS OF LIST MODE DATA
Main objectives of data analysis in -spectroscopy are: Look for -transitions following the nuclear reaction Look for correlations , etc. to search for
sequence of transitions Measure the intensity of each transition to estimate the
population of each level Extract angular distribution, angular correlation and
polarization of the gamma transitions Establish the level scheme, spin and parity of each level Additional information like life times of the states can be
extracted which gives valuable information about nuclear matrix elements
Lecture III SERC-6 School March 13-April 2,2006
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STEPS IN DATA ANALYSIS - 1STEPS IN DATA ANALYSIS - 1 Generate singles histograms (unprocessed) for each
detector Obtain the energy calibration for each detector using
radioactive sources i.e. 152Eu,133Ba, 66Ga Obtain the efficiency calibration for individual detectors
using sources with multiple transitions with known relative strength
Can be parametrized by polynomial or exponential curve
GGG
KN
KK
FyEyDcxBxA
Ea
122
0
log
loglog
Lecture III SERC-6 School March 13-April 2,2006
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Efficiency CurveEfficiency Curve
Energy in keV
x = log(E/100)
y=log(E/1000)
To simplify the procedure, most analysis programs have the option of automated search of peak centroid & area
AUTOFIT in INGASORT
Lecture III SERC-6 School March 13-April 2,2006
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STEPS IN DATA ANALYSIS - 2STEPS IN DATA ANALYSIS - 2
Next step is to do a data consistency check, i.e. look for gain drift and bad data blocks
Generate calibrated singles histograms for each detector Use strong lines in the spectra for internal calibration Programs to look for slow gain drifts exist
GAINDRIFT is a companion program for INGASORT Apply correction for Doppler effect and add-back effect
for Clover detectors Efficiency curves have to be redone if add-back is
implemented Calibrate time spectra from individual detectors
Lecture III SERC-6 School March 13-April 2,2006
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STEPS IN DATA ANALYSIS - 3STEPS IN DATA ANALYSIS - 3 In a multi-detector array, detectors placed at different
angles are essentially equivalent ( ignoring , dependence) Data from different detectors can be combined together to
make a 'Super detector' covering the full solid angle For coincidence data correlation in the super detector
1
1
N
i
N
ijijNN
2
1
1N
i
N
ijijk
N
jk
NN
Triple correlation
Greatly increased statistics due to the addition of many detector combinations
Lecture III SERC-6 School March 13-April 2,2006
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STEPS IN DATA ANALYSIS - 4STEPS IN DATA ANALYSIS - 4
The task of gain matching, add-back and search for coincidences implemented in a single command DOALL in INGASORT
To minimize repeating these steps for every analysis, desirable to create a gain-matched data tape containing the most essential information
Presently implemented in INGASORT
can be written to a disk file using DUMP command Full information or only energies possible
BIT PATTERN
SEGMENT PATTERN
MULTI-PLICITY
E1 E2 E3 T12 T13
Lecture III SERC-6 School March 13-April 2,2006
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ANALYSIS OF SINGLES DATAANALYSIS OF SINGLES DATA
The analysis of singles data is done by generating one-dimensional histograms
Additional constraints from transitions in coincidence can be put to remove unwanted background
Most direct way of visualizing higher fold correlations
Analyzing data from different angle sets ()
PRC64(2001)024304
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Background SubtractionBackground Subtraction Background from higher
energy photons under a photopeak
Need to be corrected for quantitative yield
Small region :
i. Least square peak fit with polynomial background
ii. Background estimation from in-between peaks
Large Region : piece-wise generation over the whole region
Other methods: Iterative search NIMB34(1988)396
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CorrelationCorrelation Weak peaks not resolved in singles spectrum due to large background Gating by another -ray significantly reduces the background
Singles background E
Coincidence background (E)2
coincidence conditions important for setting up the level scheme weak cross-transitions useful for establishing ordering of levels
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TWOD CORRELATIONTWOD CORRELATION
Two-dimensional histogram can be generated by storing accumulated counts in a N x N matrix
Enhanced counts at the crossing between horizontal & vertical lines : photopeak-photopeak coincidence
Matrices of dimensions 4096 x 4096 and above can be stored in memory E1 x E2
MATRIX
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Coincidence techniqueCoincidence technique
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BACKGROUND IN TWOD BACKGROUND IN TWOD CORRELATIONCORRELATION
EXPANDED VIEW
Background under E1E2 peak (A) is of three types :
b1b2 C b1p2 B - Cp1b2 D - C
Total background
= b1p2 + p1b2 +b1b2
= B + D - C One dim projection along x-axis is
made by putting a narrow gate on the peak in y-direction
Gate for y-background is subtracted from 1st projection
BA
D C
• Implemented in INGASORT
Subtract x-background from projected spectrum to get p1p2
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Cubes & hypercubesCubes & hypercubes
Many nuclei and many bands populated in one reaction For correlation, multiple gates help remove
unwanted channels
First selects nucleusSecond selects the band of interest
Desirable to have three dimensional matrix 4k x 4k x4k Would not fit into computer memory ! Writing directly on hard disk very inefficient - slow disk
access Can be implemented by maintaining a database to
minimize disk writes Database BLUE NIMA462(2001)519
Lecture III SERC-6 School March 13-April 2,2006
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High-fold MatricesHigh-fold Matrices
Analysis of matrices implemented in Radware Can be extended to hypercubes
NIMA361(1995)297
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Multi-dimensional gateMulti-dimensional gate Correlations in high fold data can be converted to one
dim histogram by setting gates on the remaining axes For double-gated coincidence, put gate 1 on x,
gate 2 on y and project z If all detectors are equivalent, add up all the
permutations of energies: 123, 132, 213, 231, 312, 321 Corresponding background gates must be subtracted
from each projection Contributions from multiple transitions can be added
in parallel to improve statistics High multiplicity events should be analyzed in their
native fold : unpacking may lead to wrong intensities
Lecture III SERC-6 School March 13-April 2,2006
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Band Identification from multi-fold dataBand Identification from multi-fold data
Yrast SD band in 149Gd
(M-1) fold gates on M-fold data improves peak to background ratio as one goes to higher fold.
Gates are put on known transitions of the SD band.
Six SD bands identified
NPA584(1995)373
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SD Bands in SD Bands in 149149GdGd
Six SD bands observed by EUROGAM
NPA584(1995)373
Thirteen SD bands have been identified by EUROGAMII in an automated database search to detect regular sequence of -transitions PRC57(1998)1151
Lecture III SERC-6 School March 13-April 2,2006
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TUTORIALTUTORIAL
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Automatic background Automatic background generationgeneration
Subtraction of background simplifies analysis
no need for setting background gates
peaks show up more clearly
can be extended to higher fold histograms
implemented in INGASORT
Lecture III SERC-6 School March 13-April 2,2006
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AUTOMATIC BACKGROUND AUTOMATIC BACKGROUND GENERATIONGENERATION
For uncorrelated background, x-projection is assumed independent of the gate in y-direction
Assume background to be a product of x & y-projections:
Bij = PiPj/T wherePi = jMij ; Pj = iMij are 1-d projT = ij Mij = total number of counts
Subtract the photopeak-photopeak part Bij = (PiPj-pipj)/T= (biPj + Pibj - bibj)/T
bi, bj are the extracted background from the 1-d total projections Can be extended to higher fold events
Radford NIMA 361 (1995) 306
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AUTOMATIC BACKGROUND AUTOMATIC BACKGROUND SUBTRACTIONSUBTRACTION
Total x-projection
gated x-projection
Lecture III SERC-6 School March 13-April 2,2006
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Multi-dimensional gateMulti-dimensional gate Background subtraction
in high-fold data can be simplified for weak gates (p « b)
Background for two-fold data = p1b2+b1p2+b1b2
b1p2+b1b2 =b1(p2+b2) background one-dim
projection n-fold background
(n-1) fold projectionNIMA355(1995)575
PRL71(1993)688
Three dimensional energy window on four fold data
Lecture III SERC-6 School March 13-April 2,2006
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Interactive Generation of level schemeInteractive Generation of level scheme
ESCL8R and LEVITSR: D.C. Radford, NIMA 361 (1995) 297-305
Assume a tentative level scheme with branching ratios for different transitions
Predict the projected spectra in coincidence with gates
Compare with the observed intensity of -lines
Adjust branching ratios to fit counts in peak Add new levels & transitions if required Continue until satisfied !! Spin & parity from angular
correlation/polarization data
Lecture III SERC-6 School March 13-April 2,2006
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Data Base Concept :Data Base Concept :
Sorting 2d matrix using Sorting 2d matrix using VAXVAX VAX only 9 MB memory How to sort 4k x 4k matrix ? From the list mode data tape, create
sixteen partially sorted list mode data so that both x & y have a range of 0-1023
Index ij of the list given by the top two bits in Ex & Ey
To improve performance, first sort into sixteen buffers in memory
Write the buffers into lists as they become full
Each list sorted separately to create 1kx1k matrix
4k x 4k matrix spread over 16 files !
Lecture III SERC-6 School March 13-April 2,2006
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Space saving with Cubes & Space saving with Cubes & hypercubeshypercubes
Saving of space possible by generating an ordered list E1 < E2 < E3 etc
matrix space is reduced by half Ordered cube needs 1/6th space Ordered hypercube needs 1/24th
space -energy resolution energy dependent
Non-linear transformation to reduce channel requirement to 1280
Features implemented in RADWARE
cEEbaE
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