EXPERIMENTS WITH LARGE GAMMA DETECTOR ARRAYS Lecture III

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


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MAIN COMPONENTS OF DATA MAIN COMPONENTS OF DATA ACQUISITION SYSTEMACQUISITION SYSTEM


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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.


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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


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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


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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.


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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


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VXI-based Front End ElectronicsVXI-based Front End Electronics


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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


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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


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ELECTRONICS FOR INGA ARRAYELECTRONICS FOR INGA ARRAY


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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 !


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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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EXPERIMENTS WITH LARGE GAMMA DETECTOR ARRAYS Lecture III

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