Detection of Explosives and Other Illicit Materials

in Containers by Nanosecond Neutron

Analysis(IAEA contract #13474)

AndreyAndrey KuznetsovKuznetsovV.G.V.G. KhlopinKhlopin Radium Institute, SaintRadium Institute, Saint--Petersburg, Russia Petersburg, Russia

apl@apl@khlopinkhlopin..ruru

Johannesburg, South Africa, 16-20 November 2009

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Presentation layout

1.1. Scope of the projectScope of the project2.2. ResultsResults�� HardwareHardware�� SoftwareSoftware�� ExperimentsExperiments

3.3. Conclusions & future Conclusions & future

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Scope1.1. Development and testing of new Development and testing of new hardware for for

compact and largecompact and large--scale NNA devicesscale NNA devices2.2. Development of new Development of new softwaresoftware for data taking, for data taking,

data analysis and decisiondata analysis and decision--making making 3.3. MCNP MCNP simulationssimulations of different geometries of different geometries 4.4. ExperimentsExperiments withwith simulantssimulants of hazardous of hazardous

materials in different scenariosmaterials in different scenarios

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Idea of NNA/APTIn NNA/APT each 14MeV neutron is In NNA/APT each 14MeV neutron is ““taggedtagged”” by by detecting an associated detecting an associated αα--particle from d+tparticle from d+t→→n+n+ααreaction in a portable neutron generator. reaction in a portable neutron generator. For each location in space (For each location in space (““voxelvoxel””) an individual ) an individual γγ--spectrum is obtained. Analysis of these spectra spectrum is obtained. Analysis of these spectra yields chemical composition of the material in yields chemical composition of the material in each voxel (3D elemental image). each voxel (3D elemental image).

segmented α-detector

neutron source(NG target)

simultaneouslyinspected volume

1 8 72 9 63 4 5

deuteronsα-particle

n eu tr on

d + t = n + α

~180otarget

vacuum

α-detector

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Challenges of NNA/APTHardwareHardware

A small DT neutron generator with intensity ~10A small DT neutron generator with intensity ~1088n/s n/s into 4into 4ππ with a builtwith a built--in positionin position--sensitive detector of sensitive detector of associated associated αα--particles is needed. particles is needed. Counting rate of Counting rate of γγ--ray detectors ray detectors –– over 10over 1055γγ/s each./s each.Counting rate of the Counting rate of the αα--particle detector particle detector –– over 10over 1066αα/s./s.Coincidence analysis of these flows of events must be Coincidence analysis of these flows of events must be performed in real time.performed in real time.

SoftwareSoftwareSpectral statistics is typically low (to reduce the Spectral statistics is typically low (to reduce the inspection time), and the number of fitting inspection time), and the number of fitting components (chemical elements) is large (>25).components (chemical elements) is large (>25).Automatic decisionAutomatic decision--making is needed to make the making is needed to make the method practical. method practical.

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Hardware: AP neutron generatorsPortable neutron generators NGPortable neutron generators NG--27 produced by 27 produced by VNIIA, Moscow, with multiVNIIA, Moscow, with multi--pixel pixel αα--detectors by detectors by APSTEC, St.APSTEC, St.--Petersburg. Petersburg. Current version produces 7Current version produces 7××101077n/s into 4n/s into 4ππ..AlphaAlpha--detectors with 9 (3detectors with 9 (3××33), 36 (6), 36 (6××6), 144 6), 144 (12(12××12), and 8 concentric pixels are available. 12), and 8 concentric pixels are available.

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Hardware: data acquisition system

Two new versions of the DAQ: compact and largeTwo new versions of the DAQ: compact and large

Older DAQ for Older DAQ for 12 12 γγ--detectorsdetectors

For 4 For 4 γγ--detectorsdetectors For 40 For 40 αα//γγ/n/n--detectorsdetectors

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Hardware: γ/neutron detectorsGammaGamma--ray detectors: ray detectors: NaINaI, BGO, LaBr, BGO, LaBr33Neutron detectors (plastic)Neutron detectors (plastic)

PCPC--controlled digital controlled digital spectrometer board spectrometer board

with HV power supply with HV power supply microchip is built into microchip is built into

the detectorthe detector

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Hardware: LaBr3 (BriLanCe) detectorResponse functions for ~30 elements measured Response functions for ~30 elements measured for BGO and for LeBrfor BGO and for LeBr33

0 1000 2000 3000 4000 5000 60000

1

2

3

4

1

2

3

4

5

BGO

Eγ [keV]

C N O F Na Mg Al Si P S Cl K Ca Ti Cr Mn Fe Ni Cu Zn As Br Cd Sn Pb

Coun

ts [a.

u.]LaBr3

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Hardware: large-scale devices

Prototype scanning module for inspection of Prototype scanning module for inspection of cargo containers:cargo containers:

Supports several neutron generatorsSupports several neutron generatorsUp to 40 Up to 40 γγ/n detectors/n detectorsEverything in standard 19Everything in standard 19”” crates (configurable)crates (configurable)

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Software: spectral regressionSpectral analysisSpectral analysis

A Partial Least Squares (PLS) algorithm has been A Partial Least Squares (PLS) algorithm has been extensively modified to make it suitable for fitting extensively modified to make it suitable for fitting spectra with low statistics. spectra with low statistics. PLS fits energy spectra of PLS fits energy spectra of γγ--rays with the response rays with the response functions for the following chemical elements: Li, Be, functions for the following chemical elements: Li, Be, B, C, N, O, F, Na, Mg, Al,B, C, N, O, F, Na, Mg, Al, SiSi, P, S,, P, S, ClCl, K, Ca, Sc, Ti, V, , K, Ca, Sc, Ti, V, Cr,Cr, MnMn, Fe, Co, Ni, Cu, Zn, and As. These response , Fe, Co, Ni, Cu, Zn, and As. These response functions has been calculated by MCNP code, or functions has been calculated by MCNP code, or measured with the respective detector. measured with the respective detector.

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Software: decision-makingAutomatic decisionAutomatic decision--making making

Based on Based on “‘“‘fuzzyfuzzy’’ logic scenarioslogic scenarios”” that that produce produce YES/NOYES/NO type of answers and type of answers and automatically determine the required automatically determine the required measurement time. measurement time. Unlike other types of neural networks, Unlike other types of neural networks, ““fuzzyfuzzy”” logic learns from human intuition. logic learns from human intuition. The approach is: one simple The approach is: one simple “‘“‘fuzzyfuzzy’’ logic logic scenarioscenario”” for each type of threat. Result of for each type of threat. Result of each measurement is then checked against each measurement is then checked against many many ““scenariosscenarios””, some of which may , some of which may produce alarms. produce alarms.

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3D visualizationThe results are visualized either as 3D spatial The results are visualized either as 3D spatial distribution of the chemical element of interest, or distribution of the chemical element of interest, or as a 3D distribution of hazardous substances as a 3D distribution of hazardous substances ((““threatsthreats””))

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MCNP simulationsSimulation of neutron beam divergence in cargoSimulation of neutron beam divergence in cargo

-200 -100 0 100 2001

10

100

1000

10000

100000

En > 10 MeV

Neutr

on flu

x (a.u

.)

X (cm)

- Y: 0 - 50 cm - Y: 50 - 100 cm - Y: 100 - 150 cm - Y: 150 - 200 cm - Y: 200 - 250 cm

CrossCross--sections of the sections of the ““taggedtagged”” neutron neutron beam at different beam at different depths in the filled depths in the filled

containercontainer

One of the One of the simulated simulated geometriesgeometries

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ExperimentsDetection of explosive and flammable liquidsDetection of explosive and flammable liquids(prototype)(prototype)

1010××1100××330cm0cm33Inspected volumeInspected volumethree three ∅∅33””××33”” BGOBGOγγ--ray detectorsray detectors

NG27 with 36NG27 with 36--pixel pixel αα--detector (pixels are joined) detector (pixels are joined)

Neutron generator:Neutron generator:

Inspection time: 1 minuteSample masses: 40g-1kg

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0-0.1

0.0

0.1

0.2

0.3

0.4

0.5

calendula

shaving foamcolacola light

cola lightshampoo

toothpaste

vodkayogurt

C4

TATP

TNT

acryl paintbenzine

deodorant

kerosinsolvent white spirit

innocuous explosives flammable

N/(C

+O)

C/(C+O)

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ExperimentsInspection of rusty chemical shells (ACWD)Inspection of rusty chemical shells (ACWD)

1010××1100××330cm0cm33Inspected volumeInspected volumethree three ∅∅33””××33”” BGOBGOγγ--ray detectorsray detectors

NG27 with 36NG27 with 36--pixel pixel αα--detector (pixels are joined) detector (pixels are joined)

Neutron generator:Neutron generator:

Inspection time: 30 seconds. Sample masses: 400g-3kg.Detected elements: C, N, O, S, As …

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ExperimentsTANISTANIS –– confirmation sensor for inspection of confirmation sensor for inspection of suitcases after Xsuitcases after X--raysrays

2020××2020××440cm0cm33Inspected volumeInspected volumetwelve twelve ∅∅33””××33”” BGOBGOγγ--ray detectorsray detectors

NG27 with 9NG27 with 9--pixel pixel αα--detector detector Neutron generator:Neutron generator:

200g RDX between book and CD pack

CN

O

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ExperimentsDetection of explosives in luggage (jointly with Detection of explosives in luggage (jointly with Bruker Daltonics Bruker Daltonics and TNO)and TNO)

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Radiography…First fast inspection First fast inspection with radiography, then with radiography, then a more detailed study a more detailed study of the suspicious area of the suspicious area with NNAwith NNADetect fissile nuclear Detect fissile nuclear materials materials simultaneously with simultaneously with other hazardous other hazardous materials in luggage materials in luggage and containersand containersCombined Combined NNA/radiography NNA/radiography device will be a device will be a universal threat universal threat detection tooldetection tool

0 5 10 15 20 25 30

100

200

300

400

500

1

10

100

NNA γ-raysfrom samples

γ-rays fromgraphite

Coun

ts (lin

ear)

Time-of-flight, ns

no sample polyethilene iron lead

14 MeVneutrons

Vγ ~ 30 cm/ns

Vn ~ 5 cm/ns

Coun

ts (lo

g)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0Experiment

poly (CH2) 8g/cm3

iron (Fe) 23g/cm3lead (Pb) 55g/cm3

high

low

Gamm

a tran

smiss

ion (γ

T/γ0)

Neutron transmission (nT/n0)

MCNP C, 5,10,20 g/cm2 Fe, 5,10,20 g/cm2 Pb, 5,10,20 g/cm2

highlow

Tmeasurement = 300 seconds

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Conclusions1.1. The hardware and software developed as the The hardware and software developed as the

result of this Project can be used to assemble result of this Project can be used to assemble devices based on Nanosecond Neutron devices based on Nanosecond Neutron Analysis (NNA) technique to detect explosives Analysis (NNA) technique to detect explosives and other threat materials in different and other threat materials in different scenarios: in suspicious unattended objects scenarios: in suspicious unattended objects and luggage, in cargo containers, in and luggage, in cargo containers, in unexploded ordnance, etc.unexploded ordnance, etc.

2.2. The chosen approach allows one to build both The chosen approach allows one to build both very compact and light systems, and very very compact and light systems, and very large automatic systems for detection of a large automatic systems for detection of a wide variety of threat materials from the same wide variety of threat materials from the same basic modules: AP neutron generators with basic modules: AP neutron generators with associated particle detectors with differentassociated particle detectors with differentpixelizationpixelization, , γγ--ray/neutron detectors with onray/neutron detectors with on--board digital spectrometers, coincidence board digital spectrometers, coincidence modules, and data analysis software. modules, and data analysis software.