GEANT4 Simulation of the Radeye Response to Point Source and Ground Deposition for the Full-Scale Radiological Dispersal Device Field Trials

Defence Research and Recherche et de´veloppement Development Canada pour la de´fense Canada

GEANT4 Simulation of the RadEye Response to Point Source and Ground Deposition for the Full-Scale Radiological Dispersal Device Field Trials

Chuanlei Liu Calian, 340 Legget Drive, Suite 101, Ottawa, ON K2K 1Y6 Lorne Erhardt DRDC – Ottawa Research Centre

Defence Research and Development Canada

Scientiﬁc Report DRDC-RDDC-2016-R005 January 2016

GEANT4 Simulation of the RadEye Response to Point Source and Ground Deposition for the Full-Scale Radiological Dispersal Device Field Trials

Chuanlei Liu Calian, 340 Legget Drive, Suite 101, Ottawa, ON K2K 1Y6 Lorne Erhardt DRDC – Ottawa Research Centre

Defence Research and Development Canada Scientiﬁc Report DRDC-RDDC-2016-R005 January 2016 c Her Majesty the Queen in Right of Canada (Department of National Defence), 2016 c Sa Majesté la Reine en droit du Canada (Ministère de la Défense nationale), 2016 Abstract

In this work, the count rate, energy spectrum, and dose rate were calculated using Geant4 simulations for the gamma sensor array that was used in trials. The buildup effect due to gamma-ray scattering in the soil was explored in detail, and the contribution proved to be significant in this case. The ambient dose rate was derived from the γ-ray flux results. Conversion coefficients were determined, allowing the calculation of dose rate and the ground deposition from count rate measurements.

Signiﬁcance for Defence and Security

This work simulated the response of the RadEye personal radiation detector to a 140La source, and explored the gamma radiation signal and energy spectrum in detail. The simulation results reveal that the presence of other materials (i.e. the ground, in this case) near the detector or source can significantly affect the detector response when taking measurements in the field. The simulation results were geared to the analysis of the FSRDD trials, but have broader application for all radiation detection measurements taken in the field.

In an operational situation, the hazard posed by radioactive contamination may be a critical consideration for decision makers. The correct interpretation of field measurements can have a large impact on the success of a military or a security mission. Therefore, thorough understating of the response of a detector system is critical for translating field measurements into an actionable hazard estimate. Simulations and field trials both aid in the interpretation of detector response in real-world scenarios, and ultimately aid decision-making.

DRDC-RDDC-2016-R005 i Résumé

La caractérisation des dispositifs de dispersion radiologique (DDR) est un domaine de recherche actif voué à la sécurité et à des fins militaires. Recherche et développe- ment pour la défense Canada (RDDC) mène des recherches dans ce domaine depuis 2003. Cependant, l’évaluation des conséquences liées aux DDR est difficile à réaliser dans les faits en raison de problèmes pratiques et de questions de sécurité relatifs à l’utilisation des isotopes radioactifs, lesquels sont considérés comme des menaces pour les DDR. À l’heure actuelle, on fait largement appel à des modèles théoriques pour caractériser et décrire la dynamique des DDR, et en tirer des quantités utiles telles que la concentration dans l’air et le dépôt au sol. Afin d’obtenir des données expérimentales sur les effets concrets des DDR, le Centre de recherches d’Ottawa de RDDC a mené une série d’essais de DDR à l’échelle réelle (FSDDR) en 2012 en utilisant une source radioactive de courte durée (140La) comme substitut pour les isotopes présentant un danger.

Dans le cadre de ces travaux, on a calculé le taux de comptage, le spectre d’énergie et le débit de dose au moyen de simulations Geant4 pour le réseau de capteurs gamma utilisé au cours des essais. L’effet d’accumulation dû à la diffusion du rayonnement gamma dans le sol a été étudié en détail et cette contribution s’est avérée importante dans ce cas. Le débit de dose ambiant a été calculé à partir des résultats des mesures du flux de rayonnement. On a déterminé des coefficients de conversion, ce qui permet de calculer le débit de dose et le dépôt au sol à partir des mesures du taux de comptage.

Importance pour la Défense et la Sécurité

Ce travail a simulé la réponse du détecteur individuel de rayonnement RadEye à une source de 140La, et a exploré en détail le signal de rayonnement gamma et le spectre d’énergie. Les résultats des simulations révèlent que la présence d’autres matériaux (par exemple, le sol dans ce cas) à proximité du détecteur ou de la source peut inﬂuer de manière notable sur la réponse du détecteur lors de la prise de mesures sur le terrain. Les résultats de la simulation ont été liés à l’analyse des essais du FSDDR, mais ils ont une application plus étendue pour toutes les mesures de détection de rayonnement prises sur le terrain.

Dans une situation opérationnelle, le danger posé par la contamination radioactive peut être un facteur déterminant pour les décideurs. L’interprétation correcte des mesures sur le terrain peut avoir une incidence importante sur le succès d’une mission militaire ou sur la sécurité. Par conséquent, une compréhension approfondie de la réaction d’un détecteur est essentielle pour traduire les mesures sur le terrain en

ii DRDC-RDDC-2016-R005 estimation du danger pouvant donner lieu à des actions. Les simulations et les essais sur le terrain aident tous deux à interpréter la réponse des détecteurs dans les scénarios du monde réel et, ﬁnalement, facilitent la prise de décision.

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iv DRDC-RDDC-2016-R005 Table of Contents

Abstract ...... i

Signiﬁcance for Defence and Security ...... i

Résumé ...... ii

Importance pour la Défense et la Sécurité ...... ii

Table of Contents ...... v

List of Figures ...... viii

List of Tables ...... x

1 Introduction ...... 1

1.1 The Full-Scale Radiological Dispersal Device Field Trials ...... 1

1.2 Motivation for this Study ...... 2

2 Ambient Dose Rate and Ground Deposition ...... 3

2.1 Fluence to Ambient Dose - H*(10)/Φ ...... 3

2.2 Air Kerma to Ambient Dose - H*(10)/Ka ...... 4

2.3 Fluence due to the Ground Deposition ...... 5

3 The RadEye Sensor Array (RSA) ...... 6

4 GEANT4 Simulation ...... 8

4.1 Radiation Source Simulation ...... 8

4.1.1 Source Simulation for Exposure Studies ...... 9

4.1.2 Source Simulation for Ground Deposition Studies ...... 9

4.2 RadEye PRD Simulation ...... 9

4.2.1 PRD Simulation for Exposure Studies ...... 9

4.2.2 PRD Simulation for Ground Deposition Studies ...... 11

4.3 Soil Simulation ...... 11

DRDC-RDDC-2016-R005 v 4.4 PRD Response Simulation ...... 12

5 Results and Discussion ...... 13

5.1 Radiation Flux (Φ) ...... 14

5.1.1 The Total Flux ...... 14

5.1.2 The Exclusive Flux From Soil Buildup ...... 15

5.1.3 The Total Flux Excluding Soil Buildup ...... 16

5.2 Energy Flux (ΦE) ...... 17

5.3 Hit and Detection Eﬃciencies ...... 20

5.4 Ambient Dose Rate - H*(10) ...... 23

5.5 Coeﬃcients Converting Count Rate to Dose Rate ...... 24

5.6 Radiation from Ground Deposition ...... 26

5.6.1 Analytical Calculations ...... 26

5.6.1.1 Fluence Result - Φunit ...... 27

5.6.1.2 Count Rate ...... 28

5.6.1.3 Dose Rate - H*(10) ...... 28

5.6.2 Geant4 Simulations ...... 28

5.6.2.1 Fluence Result - Φnorm ...... 29

5.6.2.2 Count Rate ...... 30

5.6.2.3 Dose Rate - H*(10) ...... 30

5.6.3 Comparisons and Discussions ...... 31

5.6.3.1 Fluence Result ...... 31

5.6.3.2 Count Rate ...... 31

5.6.3.3 Dose Rate ...... 32

5.6.3.4 Conversion Coeﬃcients between Dose Rate and Count Rate ...... 32 vi DRDC-RDDC-2016-R005 6 Conclusions and Future Work ...... 33

6.1 Conclusions ...... 33

6.1.1 Soil Buildup Eﬀect ...... 33

6.1.2 Ambient Dose Rate Estimation ...... 34

6.1.3 Count Rate Eﬃciency and Conversion to Dose Rate . . . . . 34

6.1.4 Ground Deposition ...... 35

6.2 Future Work ...... 35

References ...... 37

List of Acronyms ...... 39

DRDC-RDDC-2016-R005 vii List of Figures

Figure 1: The coeﬃcients converting ﬂuence to the ambient dose...... 4

Figure 2: The coeﬃcients converting air kerma to the ambient dose...... 5

Figure 3: The layout of the ﬁxed detection system in FSRDD trials. . . . . 7

Figure 5: Geant4 simulations on 140La radioactive source...... 9

Figure 6: Geant4 simulations on the RadEye PRD array...... 10

Figure 7: The simulation on PRD response to an area of ground contamination...... 10

Figure 8: A few physical quantities related to the γ transport processes inside the PRDs...... 12

Figure 9: The Geant4 simulated gamma ﬂux as a function of distance . . . . 15

Figure 10: The soil buildup contribution...... 16

Figure 11: The simulated soil-free γ radiation ﬂux as a function of the PRD distance...... 17

Figure 12: The average energy of γ radiations...... 18

Figure 13: Comparison of the energy ﬂux between Geant4 and MicroShield calculations...... 18

Figure 14: The γ energy spectrum exposed to PRDs at diﬀerent distances. . 19

Figure 15: The γ energy spectrum due to the soil buildup...... 19

Figure 16: The hit ﬂux in relative to the total ﬂux (εH)...... 20

Figure 17: The simulated hit ﬂux and the measured count rate...... 20

Figure 18: The detection eﬃciency (εD) estimated from the actual measurement...... 21

Figure 19: The detection eﬃciencies (εD) and χ2/ndf values after applying various threshold cuts...... 22

Figure 20: The detection eﬃciency (εD) after applying the energy threshold at 120 keV on the simulated hit spectrum...... 22 viii DRDC-RDDC-2016-R005 Figure 21: The calculated dose rate based on weighting each γ radiation. . . 23

Figure 22: The conversion coeﬃcients from hit/count to dose rate...... 25

Figure 23: The calculated ﬂuence for a scenario of 1 kBq 140La deposition per square metre on the ground ...... 28

Figure 24: The simulated ﬂuence for a cylindrical sensor...... 29

Figure 25: The simulated “count” rate on PRDs for 1 kBq 140La activity deposition per square metre on the ground...... 30

DRDC-RDDC-2016-R005 ix List of Tables

Table 1: The characteristic γ emissions from 140La (From ENSDF). . . . . 8

Table 2: The composition and abundance of eight chemical elements simulated in soil...... 11

Table 3: The ﬂuence calculated from the ground radiation deposition. . . . 27

Table 4: The ﬂuence results calculated from diﬀerent methods...... 31

Table 5: The count rate results from the ground radiation deposition. . . . 31

Table 6: The dose rate calculations from the ground radiation deposition. . 32

x DRDC-RDDC-2016-R005 1 Introduction

The threat of radiological attack remains an ongoing concern for civilian security and military defence. A Radiological Dispersal Device (RDD), as one form of these threats, continues to be a concern in Canada and with its allies. A simple RDD may not require much technical eﬀort to assemble and use, but could cause a widespread public disruption and economic losses [1–4].

In 2012, a series of field trials [5] using 140La were conducted in order to build up experimental data on RDD research. These trials provided, for the first time, an opportunity to study real-world RDD effects and allow for the characterization of the resulting radioactive plume and ground contamination using operational detection systems. Monte Carlo simulation has been conducted in aid of data analysis and RDD modelling.

In this report, the responses of RadEye Personal Radiation Detectors (PRDs) to the undetonated 140La point source and the post-explosive ground contamination have been studied with Geant4 simulations. These data provided a valuable reference for the system calibration and performance evaluation. The simulated response was compared to the actual measurements such that the PRD count rate eﬃciency was determined. The coeﬃcients converting the RadEye count rate measurements to dose rate and ground deposition are also determined.

1.1 The Full-Scale Radiological Dispersal Device Field Trials

In order to build up experimental data on RDD research, a series of ﬁeld trials [5] were conducted at the DRDC Suﬃeld Experimental Proving Ground in 2012, under the Centre of Security Science Project CRTI 07-0103RD “Full Scale RDD Experiments and Models” (FSRDD). These trials simulated RDDs using a series of explosive detonations of 140La, a short-lived radioactive material, as a surrogate for threat isotopes. The explosive dispersion events were exhaustively characterized using a variety of radiation detection instruments, which included an array of 250 Thermo PRDs. These PRDs were pre-positioned in an array centered around the prevailing downwind direction, at distances ranging from 10 m to 450 m from the detonation point [5]. The details of the FSRDD trials and the results of the measurements taken during the trials are beyond the scope of this paper and will be published elsewhere.

The pre-positioned array of RadEye PRDs was used to measure the background count rate, the count rate due to the intact source prior to dispersions, and the resulting count rates due to the radioactive plume and the resulting deposition. The PRDs store and transmit count rate data on a second-by-second basis before, during and

DRDC-RDDC-2016-R005 1 after the dispersion event. Backgrounds were calculated using a 10 minute average count rate, one hour prior to the source deployment. Count rate from the intact source was also a 10 minute average, taken after source deployment and just prior to dispersion. These averages were taken for multiple PRD and distance ranging from 10 m to 450 m from the source. Pre-dispersal count rate data were adjusted to correct for the background prior to being used to compare to simulation. The source activities for the three trials were, to within 10% uncertainty, 31.3 GBq, 36.3 GBq and 35.2 GBq.

1.2 Motivation for this Study The RadEye detectors used during the FSRDD field trials were operated in a non- standard way, and therefore the manufacturer specifications for energy threshold and efficiency were not applicable to the measurements taken during the FSRDD Trials. The point of the work reported in this manuscript was to use data taken during the trials, along with Monte Carlo simulations, in order to determine the low energy threshold and detection efficiency for the RadEye detectors, for the mode in which they were operated during the FSRDD Trials.

In conversation with the manufacturer, it was determined that in standard mode the RadEye does not report a true count rate. The device reports a “count rate”, Z, over a 1 s time period that is: 1) based on the higher of two 500 ms counts, 2) averaged over a time window, and 3) adjusted in order to suppress background from both cosmic rays and naturally occurring radioactive material. This smoothing and background correction is done to make the instrument more usable for non-experts, but it makes the instrument diﬃcult to use as a scientiﬁc instrument, if operated in its standard mode.

After speaking with the manufacturer, it was determined that it is possible to get true count rate data from the instrument, as these data are collected as a preliminary step prior to the application of the smoothing and background suppression algorithms. The electronics of the detector are set up to record the number of counts above three diﬀerent low-energy thresholds, with these counts designated as: Z1, Z2 and Z3. The Z1 count rate uses the lowest energy threshold (presumably ∼30 keV, as per the manufacturer specs), Z2 uses a higher threshold for counting, and Z3 uses threshold that is higher still. The three count rates are used to characterize the rough shape of the spectrum, with the diﬀerences in these rates used in their proprietary smoothing and background suppression algorithms. For data collection in the FSRDD trials, it was decided to use one of these true count rates Z1, Z2 or Z3 instead of the smoothed rate, Z, in order to ensure that we were able to interpret the results of the measurements correctly.

The manufacturer considers the energy thresholds associated with Z1, Z2 and Z3 to

2 DRDC-RDDC-2016-R005 be proprietary, and would not reveal these. For the FSRDD trials we decided to use the Z2 energy threshold because preliminary testing showed that it suppressed much of the background count rate, yet still had significant response to the 140La gamma emissions. In essence, Z2 gave the best signal to noise ratio when measuring the high- energy gammas emitted by 140La. The problem with using Z2 was that, because we were not using the RadEye in its standard operating mode, we did not know what the low-energy discriminator setting was for Z2 or what the photon counting efficiency was for the detector when operated with this threshold. We decided to use count rate data taken in the field (as function of distance from the known activity 140La source), coupled with a Monte Carlo simulation to help us estimate the Z2 low-energy threshold and the counting efficiency.

2 Ambient Dose Rate and Ground Deposition

The ambient dose rate, H*(d), is conventionally used for radiological protection and environmental monitoring purposes. As described in the ICRU Report 39 [6], H*(d) is deﬁned as the dose equivalent at depth d millimetres of an ICRU sphere in an aligned and expanded ﬁeld. In case of area or environment monitoring, the recommended depth is 10 mm and the dose equivalent is therefore denoted as H*(10).

H*(10) can be directly calculated with Monte Carlo methods based on the estimation of the absorbed dose at depth 10 mm in an ICRU sphere. This advantage of this approach is that it can be tailored to provide more flexible and detailed results. However, an ICRU sphere with a specified tissue does not seem to suit the purpose of this study. The actual sensor to be simulated is a cylindrical NaI scintillator. By simulating sensors with the actual material and geometry, the realistic effects such as the possible shielding and multiple scattering between sensors can be included; a simulated ICRU sphere is however not able to reflect these effects. For this reason, a different approach is used to calculate H*(10).

In practice, H*(10) can be derived from other measurables such as the radiation Fluence (Φ), Exposure or Air Kerma (Ka). The conversion coeﬃcients from these measurables to H*(10), together with the dosimeter calibration standards, are described in the ICRU Reports 39, 43 and 47 [6–8].

2.1 Fluence to Ambient Dose - H*(10)/Φ

The ICRU Report 47 [8] summarizes the coefficients that can be used to convert fluence (Φ) to H*(10) for mono-energetic gamma-rays (γ) up to 10.0 MeV. Its dependence on γ energy is described in Figure 1, where a fit is performed using an empirical function. The fit formula and coefficients are also provided in the figure.

DRDC-RDDC-2016-R005 3 ] 2 25 Fit function, χ2 = 0.474165 H*(10) = x + dsin(gx) 20 φ ax2+bx+c [pSv cm

φ x = Log(E /E ), E = 9.29 ± 2.55 KeV γ 0 0 15 a = 0.09 ± 0.02 b = •1.36 ± 0.23

H*(10)/ 10 c = 5.37 ± 0.80 d = 0.49 ± 0.37 5 g = 1.68 ± 0.35

0 1 10 102 103 104 Eγ [ KeV ] Figure 1: The coeﬃcients converting ﬂuence to the ambient dose. The data set is from the ICRU Report 47.

The radiation ﬂuence (ﬂux) is calculated from the number of γs incident on a unit area (and a unit time). As such, its calculation is essentially determined by the source activity and the distance between source and sensor. It is completely unrelated to the sensor in this sense.

H*(10) is derived from the gamma-ray fluence impinging on the simulated NaI scintillator, as calculated using Geant4. The conversion from fluence to H*(10) uses the fit function shown in Figure 1.

2.2 Air Kerma to Ambient Dose - H*(10)/Ka

The other approach to estimate H*(10) is using air kerma Ka. Ka can be determined either directly with Monte Carlo method or indirectly from ﬂuence. By using Monte Carlo simulations, Ka is calculated from the initial kinetic energy transferred from γs to electrons per unit mass in air. On the other hand, the relation Ka/Φ = (µtr/ρ) × Eγ allows one to derive H*(10) indirectly. Here µtr/ρ is the mass energy transfer coeﬃcient for γs with Eγ in air.

In both cases, the conversion coeﬃcients are needed to derive H*(10) from Ka. Similar to Figure 1, these coeﬃcients are available in the ICRU Report 47 [8], and their dependence on γ energy is shown in Figure 2.

4 DRDC-RDDC-2016-R005 ] •1 3 Fit function, χ2 = 0.002737 H*(10) 2.5 = x + d atan(gx) φ ax2+bx+c

x = Log(E /E ), E = 10.32 ± 1.85 KeV 2 γ 0 0 a = 1.03 ± 2.38 1.5 b = •2.87 ± 7.30 c = 3.21 ± 6.73 1 H*(10)/Ka [Sv Gy d = 0.77 ± 1.51 g = 0.34 ± 2.11 0.5 0

1 10 102 103 104 Eγ [ KeV ] Figure 2: The coeﬃcients converting air kerma to the ambient dose. The data set is from the ICRU Report 47.

However, neither of these two methods is adopted in this study. The Monte Carlo method requires a conﬁguration of an air volume to calculate the deposited energy, while the indirect method essentially relies on the ﬂuence calculation while introducing one additional parameter (µtr/ρ).

2.3 Fluence due to the Ground Deposition

One possible radiation source that can contribute to the sensor exposure is from the experimental ﬁeld in FSRDD. This arises either from the naturally occurring radionuclides in soil or the ground contamination from an RDD. In this study, the contribution from the naturally background radiation has not been estimated. How- ever, the approach to estimate it with Geant4 is described in Section 4.

The sensor response to a ground contamination was estimated by using an analytical method [9] and Geant4 simulations. In the analytical method, the ground was assumed to be contaminated uniformly over a sensor-centred disc. The disc has a radius of R, and will turn to an inﬁnite plane if R approaches inﬁnity. The activity distributed on the ground is A Becquerel per unit area.

DRDC-RDDC-2016-R005 5 By integrating contributions all over the source disc, the total Φ through the sensor can be expressed as A √ Φ = [E (µ h) − E (µ h2 + R2)], (1) 2 1 a 1 a Z ∞ e−zt Z ∞ e−u where E1(z) = dt = du. (2) 1 t z u −z 0 e E (z) = −E (z) = (3) 1 0 z

The variable µa in Equation 1 is the γ-ray linear attenuation coefficient in air, and h is the altitude that the sensor is above the ground. The E1(z) function is the Exponential Integral function in mathematics, which integrates over an open interval from z to infinity (the last formula in Equation 2). Equation 3 describes the first derivative of E1(z), which will be used in the variance estimation.

In case of using Geant4, the sensor fluence and the hit efficiency were estimated. To estimate Φ, the sensor volume is projected to a plane that is normal to the γ incident direction so that the effective area of the sensor is calculated and normalized to a unit area. To verify the normalized fluence, a spherical PRD was simulated as well. The hit efficiency is needed to convert fluence to count.

3 The RadEye Sensor Array (RSA)

The FSRDD trials used several detection networks to monitor and measure the pas- sage and deposition of the radioactive plume. These networks include two fixed γ sensor arrays (the RadEye [10] and RS250 arrays), a set of ground deposition filters and two mobile survey systems. Regarding the RSA network, it consisted of 250 Per- sonal Radiation Detectors (PRDs) that were placed at 1 m height above the ground, facing to the source and lying on their side. The RSA was configured into rings and arcs from 10 to 450 m away from the source, horizontally covering 120◦ of the field of view. The layout of the RSA is illustrated in Figure 3.

Inside each PRD is a cylindrical sodium iodide (NaI) scintillator. It has a radius of 0.9 cm and a height of 3.1 cm. The scintillation light produced from NaI is collected by a Photo-Multiplier Tube (PMT) to which it couples. Due to the lack of information needed to perform a full PRD simulation (i.e. the speciﬁcations of the coating material and the PMT), only a bare NaI crystal was simulated.

6 DRDC-RDDC-2016-R005 Figure 3: The layout of the ﬁxed detection system in FSRDD trials. The data loggers with blue squares represent the RadEye Sensor Array. The plot is extracted from [5].

Figure 4: The RadEye PRD used in FSRDD trials.

DRDC-RDDC-2016-R005 7 4 GEANT4 Simulation

The Monte Carlo method was used to investigate the PRD response when exposed in a radiation field produced by a stationary point source or an area of ground contamination. The number of γs incident on PRDs was computed in Geant4 after taking into account the effects of absorption and scattering in their transport processes. The simulation also calculated the ambient dose based on the conversion coefficients described in Section 2.1. With these results, the relation between source activity, fluence and ambient dose was established. The relation was used afterwards to convert count rate to dose rate and ground radiation deposition or vice versa.

Geant4 [11, 12] simulation toolkit was used for simulating radiological sources, their decays and the subsequent transport processes. The simulations were conducted at the local computer cluster [13] at DRDC Ottawa.

4.1 Radiation Source Simulation Geant4 has a few of built-in event generators (for the primary particles and vertices) such as the G4GeneralParticleSource and G4ParticleGun classes. It can also interface to other event generators such as HEPEVT and HEPMC to read in events from them. This study used the G4GeneralParticleSource class to simulate the radioactive decay of Lanthanum-140.

The isotope 140La was initially specified in a configuration file, and its decay chain was simulated based on the Evaluated Nuclear Structure Data File (ENSDF) data library [14]. ENSDF provides the pre-determined data sets relevant to 140La and its daughter’s decay, such as its half-life, decay modes, branching ratios and emission energies. By sampling the available decay channels according to their branching ratios (probability), a decay event is initiated in Geant4.

140La is a beta and gamma emitter. It undergoes a beta decay, and its daughter 40Ce subsequently releases γ- and X-rays to reach a stable state. There are tens of beta decay modes for 140La and more than ﬁfty γ- and X-rays produced from the 40Ce de-excitation processes. Table 1 lists the four characteristic γ radiations which have a relatively larger intensity than the rest γ-rays. The full γ- and X-ray spectrum generated from Geant4 is shown in Figure 5.

Table 1: The characteristic γ emissions from 140La (From ENSDF). Energy (MeV) Yield (Bq−1s−1) 0.3288 0.203 0.487 0.455 0.8158 0.233 1.596 0.954

8 DRDC-RDDC-2016-R005 1 1 La140 Mean 1.063 La140 Mean 2.421 RMS 0.5692 10•1 10•1 10•2 •2 10 10•3 Relative intensity •4 •3 10 10 10•5 •4 10 •6 Fraction of Remaining Activity 10 0 0.5 1 1.5 2 2.5 3 3.5 0 10 20 30 40 50 The γ energy spectrum [ MeV ] Radionuclide Life Time [ days ] Figure 5: Geant4 simulations on 140La radioactive source. The simulated energy spectrum and life time are shown at the left and right plots, respectively.

The half-life of 140La is shown in Figure 5. A simple calculation from the mean life time (2.421 days) leads to a half-life of 1.6788 days. This result is consistent with 1.6781 days given in the ENSDF database.

4.1.1 Source Simulation for Exposure Studies

To simulate the exposure measurements in FSRDD, the 140La source was conﬁgured to be a point source which was placed at 1 metre above the ground.

4.1.2 Source Simulation for Ground Deposition Studies

For the ground deposition studies, a disc source was simulated; the disc sat right on the ground surface and the 140La source were uniformly distributed across it. The disc radius was set to be 200 m such that it is large enough to see the asymptotic trend of the sensor response as the source size expands.

4.2 RadEye PRD Simulation

In the FSRDD trials, a total of 250 RadEye PRDs were deployed 1 metre above the ground, forming shapes of circles or arcs depending on its distance from the source.

4.2.1 PRD Simulation for Exposure Studies

For the point source simulation, the RSA was conﬁgured to have sixteen coaxial PRD rings in a plane, as shown in Figure 6. The rings were placed 1 metre above the ground, and had radial distances from 10 metres to 400 metres. Each ring consisted of 360 PRDs uniformly separated in the azimuthal direction. The PRDs at the outer rings were positioned at well calculated angular oﬀsets relative to PRDs from the

DRDC-RDDC-2016-R005 9 inner rings. The position shifting was for the purpose of avoiding the inter-shielding eﬀects between PRDs in diﬀerent rings.

500 400 300 200 100 0 •100

•200Sensor y Position [ m ] •300 •400 •500 •400 •200 0 200 400 Sensor x Position [ m ] Figure 6: Geant4 simulations on the RadEye PRD array. The left plot shows the sixteen PRD rings simulated in this study, and the right plot allows a close look at the PRDs in a ring. The tiny brown cylinders in the right plot represent the simulated NaI crystals.

With this configuration, the density of the simulated RSA is 1 PRD per azimuthal degree. In reality, its density is 1/7.5 PRD per degree for most of cases. The increasing density in simulations was to improve the hit efficiency on these tiny PRDs. Given the final results were normalized to 1 PRD per full circle, the impact due to using a different density in simulation is expected to be small.

Figure 7: The simulation on PRD response to an area of ground contamination. The dark grey part at the bottom is the simulated soil, while the light grey area in the middle is source disc. The brown square is the cross-section view of the simulated PRD (the size is enlarged by 10 times for the visualization purpose).

10 DRDC-RDDC-2016-R005 4.2.2 PRD Simulation for Ground Deposition Studies

Figure 7 shows the conﬁguration for the ground deposition simulation; only one PRD is simulated and it is at one metre above the ground.

4.3 Soil Simulation

A detailed soil simulation is necessary for radiation studies if the soil buildup (scattering or diﬀusion) is relevant or the background radiation from soil is not negligible. Correspondingly, a soil simulation includes two main parts; one is the soil’s chemical composition and the other is soil’s radionuclide concentration. The chemical composition should be simulated as close to reality as possible in order to reﬂect the actual γ-ray reactions with soil, while its radionuclide concentration is important for the background radiation estimation.

Table 2: The composition and abundance of eight chemical elements simulated in soil. Element Abundance (%) Hydrogen (H) 2.1 Carbon (C) 1.6 Oxygen (O) 57.7 Aluminum (Al) 5.0 Silicon (Si) 27.1 Potassium (K) 1.3 Calcium (Ca) 4.1 Iron (Fe) 1.1

The chemical composition considered in this report is listed in Table 2, together with the abundance of each element. The soil simulated this way has a density of 1.5 g/cm3 [15]. It shows in study [16] that, for a variety of soils, the mass attenuation coeﬃcient is at a level of 0.1 cm2/g for photons at MeV. As such, a simple calculation suggests that the 1.0 (3.0) MeV photons have a mean free path of 0.07 (0.1) m in the soil simulated in this study. The thickness of the soil was set to 10.0 metres in this study, which should be enough to contain most of the possible diﬀused photons from soil to sensor.

The naturally existing radionuclides in soil usually refers to several long life-time radioactive isotopes such as Potassium (40K), Uranium (235U and 238U) and Thorium (232Th) and their unstable decay products. Their concentrations in soil vary at diﬀe- rent geographic locations, consequently leading to various background radiation levels. In Monte Carlo, the background radiation can be modelled by distributing these radionuclides into soil according to their concentrations measured in the environment. However, the simulation is a time consuming process because of the complexity of the

DRDC-RDDC-2016-R005 11 decay modes of these sources and the transportation process of their decay products inside soil. Because it is believed to be less relevant than other contributions, the background radiation was not modelled or estimated.

4.4 PRD Response Simulation In this study, detailed particle kinematics for electrons and γs were tracked and recorded for each decay process, especially inside the NaI crystals. In addition, other quantities were derived to characterize the RadEye response and the underlying physics processes. This kind of information is not necessarily required to explain the overall proﬁle of the sensor response, however it provides valuable information for understanding and verifying the simulation.

3 10 2 Mean 1.002 10 Mean 0.3061 RMS 0.03875 RMS 0.3523 102 10 Relative intensity Total Hits Total Per GBq 10

1 (a) 1 (b) 0 0.5 1 1.5 2 2.5 3 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 Total deposited energy [ MeV ]

Mean 3.575 Mean 0.2584 RMS 4.804 102 RMS 0.4185 102

Relative intensity 10 Relative intensity 10

1 (c) 1 (d) 0 5 10 15 20 25 30 0 0.5 1 1.5 2 2.5 3 Number of transport steps Sum of step length [ cm ] Figure 8: A few physical quantities related to the γ transport processes inside the PRDs. Plot (a) is the hit multiplicity observed by sensors per GBq activity.

As an example, Figure 8 shows four quantities that are relevant to the γ transport processes in the PRDs. All distributions are normalized to one Giga-Becquerel activity and one PRD at a speciﬁc distance (therefore, RSA has 16 PRDs in total).

Figure 8(a) displays the PRD hit multiplicity. The total number of hits was approximately 1 x 103, equivalent to a hit probability at an order of 10−6 (103 hits/GBq).

12 DRDC-RDDC-2016-R005 This probability is essentially the order of the solid angle coverage by the RSA in a 4π space.

Figure 8(b) shows the energy spectrum deposited in the PRDs. In approximation, the spectrum is the result of PRD attenuation of the primary 140La γs. The visible spikes around 0.5, 0.8 and 1.6 MeV are from the characteristic γs of 140La. The absorption eﬃciencies for NaI are about 45% and 10% for 0.5 and 1.6 MeV γ radiations, respectively. A rough estimation suggests that about 20% (45% x 0.455) and 9.5% (10% x 0.954) of the 0.5 and 1.6 MeV γs were stopped by the PRDs. Therefore, the relative intensity at these two energies in Figure 8 (b) should be close to 2:1. The similar ratio is indeed seen in the ﬁgure.

Figures 8(c) and 8(d) provide additional details of the γ transport processes inside the PRDs. These two distributions are the number of interactions and the total path- length that radiation travels inside the PRD. These distributions are unique because they represent the specific features of NaI response (with a specific configuration in dimension and distance) to 140La radiation.

5 Results and Discussion

This section presents the simulation results in terms of sensor radiation flux, energy flux and dose rate as a function of the sensor distance. The detection efficiency was estimated and the coefficients converting count rate to both dose rate and ground deposition were derived.

The relation between sensor count (count rate) and ﬂuence (ﬂux) is:

count = ﬂuence(Φ) · Seﬀ · εH · εD. (4)

Here variable Seff is the effective acceptance area of the sensor normal to the source direction and εH (εD) is the hit (detection) efficiency.

The calculation of Seff depends on the situation how the sensor is projected to the plane normal to the source direction. For the exposure studies, Seff is the cross section of the cylindrical PRD along its central axis. However, it becomes less straightforward for the ground deposition study because of the source location and orientation varies with respect to the PRD. In this case, the projected area of the PRD’s two surfaces and central cross section was mathematically calculated with respect to the source direction, and summed up as Seff .

A hit refers to a γ that interacts with a sensor rather than just passing through it without interaction. The hit efficiency εH describes the fraction of the initial fluence that make hits. The detection efficiency (εD) is derived by comparing the real count

DRDC-RDDC-2016-R005 13 measurement to the simulated hits. Here εD was introduced primarily accounting for the counting loss of photons in a real PRD system (i.e. due to the imperfect light collection/conversion eﬃciency, or any electronic losses).

For exposure studies with a point source:

• A total of 5.7 x 109 140La disintegrations were simulated. If taking into consideration that 360 PRDs were coaxially simulated at a speciﬁc distance, the equivalent 140La activity simulated thus turns to 205.2 x 1010 Becquerel (∼55.5 Curie) for a single PRD at that distance. • The primary radiations feature an isotropic emission into a 4π space. • The results are given and discussed in Section 5.1 to 5.3. Except where explained speciﬁcally, all results in those sections are normalized to one GBq activity.

For ground contamination studies:

• For ground contamination studies, a total of 7.0 x 109 disintegrations were simulated. The source was distributed on a disc with radius of 200 m. This corresponds to a deposition density of 55.6 kBq per square metre on the ground. • The results are shown in Section 5.6.

5.1 Radiation Flux (Φ)

In this section, the γ-ray flux is defined as the number of γ-rays incident on a PRD in one second. Note the conventional way to define flux is to normalize the number of γs to a unit area.

The simulated Φ distributions are given in Figure 9, where the overall γ ﬂux (blue points) as well as the individual contribution from the soil buildup (green squares) are presented.

5.1.1 The Total Flux

Figure 9 shows the overall ﬂux drops as distance increases. This tendency is controlled by the inverse square law of the radiation, while the other processes such as the air attenuation/buildup and multiple scattering also have an inﬂuence.

The effect of the γ-ray multiple scattering between sensors were also investigated; the resultant flux is found to be about 4 orders of magnitude smaller than the overall flux. As such, the multiple scattering flux will be neglected hereafter.

14 DRDC-RDDC-2016-R005 103 Total La140 Soil Buildup 102

/Sensor/sec] Geant4 Simulation •1 10

10•1

Gamma Flux [ GBq 0 100 200 300 400 500 Sensor to Source Distance [m] Figure 9: The Geant4 simulated gamma ﬂux as a function of distance. The contribution from the soil buildup is also provided.

5.1.2 The Exclusive Flux From Soil Buildup

The presence of the ground introduces extra γ signals in PRDs. These extra γs strike sensors after deflecting at the surface of ground or scattered-back (diffused) from the interior of soil. Other γ transport processes in soil, such as transmission or absorption, are not expected to contribute to the extra exposure. In the following, a general term “soil buildup” will be used to describe the combined contributions of the soil reflected and diffused γ-rays.

The γ flux induced from soil buildup is shown in Figure 10. The fit function seen in the left plot represents the inverse square law and it also includes the air attenuation effect. The fit gives an overall activity contribution of 0.87 GBq and a very small mass attenuation effect (the attenuation coefficient is the same to that for ∼10.0 MeV γs). The cause of small attenuation effect is under investigation (Note that the distribution is obtained by assuming a negligible impact from the stand used to support the source).

The right plot in Figure 10 shows the relative contribution of the soil buildup to the total ﬂux. A sizable contribution is found through the whole distance range and it varies from 25% to ∼40%. The distribution exhibits an increasing trend as the distance increases from 10 to ∼100 metres, and then statistically reaches a plateau that extends to all farther distances.

DRDC-RDDC-2016-R005 15 1 105 Soil Buildup Only 0.9 4 10 A = 0.87 +/• 0.04 0.8 3 0.7

/Sensor/sec] 10 •1 0.6 102 0.5 10 0.4 0.3 Soil Buildup / Total Flux 1 0.2 10•1 La140 0.1 La140

Gamma Flux [GBq 0 0 100 200 300 400 500 0 100 200 300 400 500 Sensor to Source Distance [m] Sensor to Source Distance [m] Figure 10: The soil buildup contribution. The left plot shows a ﬁt on the soil buildup contribution with a function of the inverse square law, while the right shows the relative contribution of the soil buildup with respect to the total ﬂuence.

5.1.3 The Total Flux Excluding Soil Buildup

After excluding the soil buildup contribution, the total flux is then expected to be mainly from the primary γ-rays (and the scattered γs in air). The resultant flux after subtraction is given in Figure 11. Three functions have been drawn in an attempt to characterize the flux distribution. The function by assuming a mono-energetic γ-ray at 1.59 MeV (the most probable γ emission from 140La) is found to be close to the simulation, but it underestimates the simulated flux. The discrepancy may be interpreted by the actual composition of the 140La γ-rays and the air buildup effects.

In theory, the mass attenuation coefficient for 1.59 MeV γs is reported as µ/ρ0 = 5.03× 10−2 cm2/g in NIST [17] in air. At a typical room condition, the air density is about ρ = 1.293 × 10−3 g/cm3. Therefore, its linear attenuation coefficient in −3 −1 air is µ/ρ0 × ρ = 6.5 × 10 m (See Table 3 for more details). Its HVL (Half Value Length) and MFP (Mean Free Path) are then derived as ∼100 m and 144 m, respectively. These values help to explain the attenuation effects. For example, the 1.59 MeV flux with attenuation should be approximately half of the flux without attenuation at 100 m.

16 DRDC-RDDC-2016-R005 Fit (Assume Eave = 1.59 MeV ) Geant4 (Total • Soil Buildup) γ 3 Fit (Assume Eave = 0.5 MeV ) 10 Fit (No Attenuation) γ A = 2.42 ± 0.04 (No Attenuation) 2

/Sensor/sec] 10 •1 Flux = A S e•µ/ •ρ d 4πd2 10

10•1 La140

Gamma Flux [GBq 0 100 200 300 400 500 Sensor to Source Distance [m] Figure 11: The simulated soil-free γ radiation ﬂux as a function of the PRD distance.

5.2 Energy Flux (ΦE)

In addition to Φ, the radiation energy ﬂux (ΦE) is the other quantity of importance to consider for dose estimation. In the following, the average γ energy incident on PRD, the energy ﬂux and energy spectra are presented in Figures 12-15.

The average energy of all γ-rays that land on PRD is shown in Figure 12. In general, the average energy drops smoothly from ∼0.8 MeV at 10 m to ∼0.5 MeV above 100 m. A similar trend is found in the soil case; it drops from ∼0.35 to ∼0.17 MeV. The similar trend indicates a close correlation between the soil ΦE and the total ΦE.

The energy ﬂux ΦE was calculated by summing up energy of all γ-rays that land on a unit area. The simulated ΦE results are given in Figure 13, together with two MicroShield calculations for comparison purpose. At near distances, the air buildup [18, 19] does not expect to have large impact on the results. Therefore, all results seem to agree well in that region. As the impact of air buildup becomes stronger and stronger, the two MicroShield calculations diverge and the simulated ΦE is found to sit between them. The comparison suggests that the MicroShield results with air buildup overestimates the simulated ΦE which even includes the soil buildup contribution. Here the soil buildup (green squares) contributes approximately 10% to the total ΦE (blue dots).

DRDC-RDDC-2016-R005 17 103 Total Soil Buildup Energy [ KeV] γ Average 102 Geant4 Simulation 0 100 200 300 400 500 Sensor to Source Distance [m] Figure 12: The γ energy in average for the total and soil-induced ﬂux.

Geant4 Micro Shield • Buildup 102 Geant4 Soil Buildup Micro Shield • No Buildup /sec ] 2 10

10•1

10•2 Energy Flux [ MeV/cm γ 0 100 200 300 400 500 Sensor to Source Distance [ m ] Figure 13: Comparison of the energy ﬂux between Geant4 and MicroShield calculations.

18 DRDC-RDDC-2016-R005 6 10 Geant4 Simulation La140 5 Entries 10 10 m 4 10 50 m 103 200 m 102 300 m

10 400 m 1

102 103 Gamma Energy Spectrum [ KeV] Figure 14: The γ energy spectrum exposed to PRDs at diﬀerent distances.

6 10 Geant4 Simulation La140 5 Entries 10 10 m 4 10 50 m 103 200 m 102 300 m 10 400 m 1

102 103 γ Spectrum BackScattered from Soil [ KeV] Figure 15: The γ energy spectrum due to the soil buildup.

DRDC-RDDC-2016-R005 19 Figure 14 shows the energy spectra of γs incident on PRDs at ﬁve radial distances. For all spectra, the signature γs from 140La are seen as these discrete spikes above ∼0.3 MeV. The continuous γ spectrum on which these signature γs sit represents the contribution from other sources. As the distance increases, the characteristic γs become less dominant with respect to the continuous spectrum.

The continuous spectrum seen in Figure 14 seems to be largely related to the soil buildup contribution, as suggested in Figure 15. The shape and magnitude of the continuous spectrum both agree well between two ﬁgures.

5.3 Hit and Detection Efﬁciencies

The hit eﬃciency, εH, tells the probability that the γs incident on a sensor will interact with the sensor so as to deposit energy in it. The eﬃciency εH depends on the sensor used (material and geometry) and the energy of the incident γs.

The hit ﬂux, with respect to the total Φ, is shown in Figure 16. The result suggests an εH of about 40% at 10 metres, and ∼60% at 100 metres and above. The close- by PRDs experience an abundance of higher energetic γ-rays than those at further distance. Therefore the incident γs undergo less attenuation, resulting in a relatively lower hit ﬂux for the nearby PRDs.

Figure 17 elaborates on the results on the simulated hit flux and the actual count measurement. The measured count rate was obtained by averaging the three expo- sures from the FSRDD trials. By assuming that Geant4 is able to describe the actual hit flux on a pure crystal, then the ratio between the two distributions in this figure is the detection efficiency, εD. The distance dependent εD is given in Figure 18, where an overall efficiency is found to be about 35.9%.

1 0.9 Mean 149.8 103 Geant4 Simulation Three Shots Average 0.8 102 /Sensor/sec]

0.7 •1

0.6 10

Hit Flux / Total 0.5 0.4 1

0.3 Hit Flux [ GBq La140 10•1 0.2 0 100 200 300 400 500 0 100 200 300 400 500 Sensor to Source Distance [m] Sensor to Source Distance [m] Figure 16: The hit flux in relative to the Figure 17: The simulated hit flux and the total flux (εH). measured count rate.

20 DRDC-RDDC-2016-R005 In practice, there are at least two factors that influence the measured count: the detection system/efficiency and the energy threshold. These two aspects function differently but are expected to have a similar impact on the measured count (and the energy spectrum). The system (i.e. the coating material) will shield/filter the low energy γ-rays naturally while the threshold will cut off low γs intentionally.

Figure 18 gives the εD distribution that is essentially the ratio between the two distributions in Figure 17. εD becomes smaller as distance increases. This tendency can be explained by looking at the hit ﬂux in 17, and the energy spectra in Figures 14 and 15. The hit ﬂux used to derive εD includes these γ-rays at energy down to tens of keV, which are believed to be largely excluded in the measurements as a result of either the implemented energy threshold or the other natural shielding factors. 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

RadEye Detection Efficiency 0.1 0 0 100 200 300 400 500 Sensor to Source Distance [m]

Figure 18: The detection eﬃciency (εD) estimated from the actual measurement.

In order to understand the energy threshold used in RadEye and the associated impacts, various cutoff values were applied on the deposited energy of each γ hit to see the changes on the hit flux. The detection efficiency was also re-estimated as the hit flux changed, and the results are given in Figure 19.A χ2/ndf value was computed to describe how well the hit distribution with a specific energy cut can describe the measured counts. The results show that a cutoff value at 120 keV makes the closest similarity between distributions of the simulated hit and measured count. The εD distribution after applying this threshold is shown in Figure 20, where the overall εD is 59.1 ± 2.1 %.

DRDC-RDDC-2016-R005 21 120 6

100 Efficiency 5 Chi2/NDF 80 4

60 3

40 2 Detection Efficiency [%] 20 1

0 0 50 100 150 200 Energy Threshold [KeV]

Figure 19: The detection eﬃciencies (εD) and χ2/ndf values after applying various threshold cuts.

1 0.9 eff = 59.08 +/• 2.07 % 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Energy Threshold at 120 KeV

RadEye Detection Efficiency 0.1 Chi2/NDF = 0.26 0 0 100 200 300 400 500 Sensor to Source Distance [m]

Figure 20: The detection eﬃciency (εD) after applying the energy threshold at 120 keV on the simulated hit spectrum.

22 DRDC-RDDC-2016-R005 5.4 Ambient Dose Rate - H*(10)

As discussed in Section 2.1, the dose rate estimation in this study is based on the Φ simulation and the conversion factors between Φ and H*(10). The factor varies for diﬀerent γ energies.

Two different approaches have been used to derive H*(10). The first approach starts with finding the conversion factor for each incident γ-ray according to its energy, and then sums up all factors to get H*(10). In contrast, the second approach averages the energy of all incident γ-rays first, and then uses it to determine a conversion factor. By multiplying this single factor with the total number of incident γs, the dose rate is obtained.

La140 Geant4.9.4 Simulation 1 Geant4 Soil Buildup Micro Shield • Buildup

Sv/hr/GBq] •1

µ 10

10•2 H*(10) [ 10•3

10•4 0 100 200 300 400 500 Sensor to Source Distance [ m ] Figure 21: The calculated dose rate based on weighting each γ radiation.

Figure 21 shows the H*(10) result from the ﬁrst approach. In overall, the Geant4 and MicroShield predictions are similar; 6.14 µSv/hr in Geant4 and 4.19 µSv/hr from MicroShield. The total H*(10) from the second approach is 5.53 µSv/hr.

Speciﬁcally, MicroShield seems to underestimate H*(10) from 10 metres to ∼100 metres. The air buildup correction is responsible for the excess in MicroShield over the Geant4 result at greater distances. However, an over-correction may be the case. Furthermore, Geant4 employs a detailed physics and γ transport simulation while MicroShield is believed to highly rely on a mathematical model and pre-determined

DRDC-RDDC-2016-R005 23 database. In addition, the inclusion of soil impact in Geant4 also accounts for the discrepancy on H*(10) results.

The soil induced H*(10) amounts to about 11.2% of the total H*(10). Because the γ-rays associated with soil buildup are usually characterized by low energies in average, its H*(10) contribution thus shrinks in comparison to its contribution to Φ (i.e. Figure 10).

5.5 Coefﬁcients Converting Count Rate to Dose Rate

For a counting instrument, it is useful to get the coeﬃcients, α, to convert the measured count rate to dose rate or vice versa.

In Geant4, α was estimated at three different stages with a purpose of illustrating the relative changes at each stage. The first stage uses the simulated hit number, and the second uses the same hit number but with an energy cutoff. At the third stage, the detection efficiency is additionally applied on the number from the second stage. At each stage, coefficient was calculated as the ratio between the γ hits/counts and the simulated H*(10). For simplicity, the corresponding coefficients for these three stages are denoted as αhit, αcut and α, respectively.

The conversion coeﬃcient was also calculated by taking the ratio of the actual count rate measurement and the simulated H*(10). The coeﬃcient obtained with measurement is denoted as αdata.

Figure 22 shows the results on αhit (black triangle), αcut (purple rectangle), α (blue solid circle) and αdata (red open circle) as a function of the sensor distance. The energy cut at 120 keV ﬂattens out the distribution, and turns αhit into αcut. The detection eﬃciency (59.1%) changes the hit number into the simulated count, and leads to the α distribution.

The α results predict the conversion coefficients at a range of 60 to 80 cps per µSv/hr. The αdata was found to have a similar feature to α, but only suffers more statistical fluctuations at the same range. By averaging all PRDs at the different distances, the overall conversion coefficient for α (αdata) is 60.58 (60.43) cps per µSv/hr.

24 DRDC-RDDC-2016-R005 3 •1 10 Geant4: Hit Flux/H*(10) Geant4: Hit Flux (& Energy Threshold) /H*(10) Geant4: Simulated Count/H*(10)

Sv/hr] Data (Shots Average)/H*(10) µ

102 Count Rate / H*(10) [ 0 100 200 300 400 500 Sensor to Source Distance [ m ] Figure 22: The conversion coeﬃcients from hit/count to dose rate.

DRDC-RDDC-2016-R005 25 5.6 Radiation from Ground Deposition

This section presents the results on the PRD response in the presence of ground contamination. The ﬂuence was calculated, and the conversion coeﬃcients from the ground deposition to count rate and dose rate were also derived. Two methods were used to perform these calculations; one used Geant4 while the other is an analytical calculation based on Equation 1.

In Geant4, the fluence was estimated from the number of incident γ-rays on the sensor. For each γ-ray, its count was weighted according to an effective PRD area, calculated by projecting the sensor to a plane that is normal to the direction of the incident γ-ray. The weighted fluence has a unit of m−2s−1, and will be compared to the analytical result. To summarize, the fluences obtained in various ways are denoted as: Geant4 per PRD: ΦPRD

Geant4 normalized: Φnorm (5)

Analytical Method: Φunit

The fluence ΦPRD represents the original number of incident γ-rays on the PRD. It has a unit of per sensor per second, and is used to estimate εH and the conversion factor between count rate and ground deposition. The weighted fluence, Φnorm, can be expressed as Φnorm = ΦPRD/Seff . Here Seff is the effective PRD cross section and was estimated in simulations.

As Equation 4 shows, at least two more quantities are needed for converting the fluence to count or vice versa: εH and εD. The value of εH is from the simulation results in Section 5.6.2, while the detection efficiency εD is from Section 5.3. The results on these two efficiencies are: ε = 0.474, A (6) εD = 0.359.

5.6.1 Analytical Calculations

In this method, the ﬂuence Φunit was analytically calculated using Equation 1. Here the 140La deposition on the ground is assumed to have an activity of 1 kBq per square metre, and the sensor is at 1 metre above it.

26 DRDC-RDDC-2016-R005 Table 3: The fluence calculated from a ground deposition of 140La on an infinite circular disc. Here BR is the branching ratio for a γ-ray with a specific energy. µm (µa) is the mass (linear) attenuation coefficient. 2 −1 −2 −1 γ (MeV) BR µm (cm /g) µa (m ) Φ (m s ) 0.329 0.203 0.1034 0.0134 380.667 ± 19.511 0.288 0.008 0.1087 0.0141 14.659 ± 3.829 0.433 0.029 0.0927 0.0120 55.947 ± 7.480 0.487 0.455 0.0882 0.0114 889.032 ± 29.817 0.447 0.004 0.0916 0.0118 8.536 ± 2.922 0.752 0.043 0.0731 0.0095 88.632 ± 9.414 0.816 0.233 0.0702 0.0091 481.650 ± 21.947 0.775 0.001 0.0720 0.0093 1.960 ± 1.400 0.868 0.055 0.0683 0.0088 114.427 ± 10.697 0.920 0.027 0.0664 0.0086 55.705 ± 7.464 0.925 0.069 0.0663 0.0086 144.591 ± 12.025 0.951 0.005 0.0653 0.0084 10.912 ± 3.303 1.600 0.954 0.0503 0.0065 2129.694 ± 46.149 2.350 0.008 0.0414 0.0054 19.770 ± 4.446 2.520 0.035 0.0400 0.0052 81.194 ± 9.011 2.550 0.001 0.0397 0.0051 2.373 ± 1.541 all 4479.75 ± 66.93

5.6.1.1 Fluence Result - Φunit

140 The Φunit result for the individual La γ-ray energies is given in Table 3. All the γ-ray branching ratio shown in the table amount to about 99.5% of total γ energy flux of Lanthanum-140. The mass (linear) attenuation coefficients and the branching ratios were taken into account when performing the fluence calculation. In addition to the table, the distribution of the accumulated fluence, as a function of the source disc radius, is given in Figure 23 for the four primary γ-rays of 140La.

−2 −1 For all γ-rays, Φunit asymptotically approaches 4386.34 and 4479.75 ± 66.93 m s for a disc of radius 200 m and infinity respectively. For comparison, the fluence calculated in MicroShield gives 4574 (5497) m−2s−1 for a scenario without (with) buildup effect. The disc source used in MicroShield has a radius of 200 m, and 1 2 140 kBq/m La activity. The analytical result, Φunit, agrees well with the MicroShield calculation that does not consider the air buildup effect.

DRDC-RDDC-2016-R005 27 104 Asymptote Deposition 2 103

Total (16 γs) 1.596 MeV 0.4870 MeV 102 0.8158 MeV 0.3288 MeV Fluence for 1 kBq/m 0 100 200 300 400 500 R (m)

Figure 23: The calculated ﬂuence for a scenario of 1 kBq 140La deposition per square metre on the ground. The calculation uses Equation 1 and the total ﬂuence from the four dominant γ-rays of 140La is 3881 m−2s−1.

5.6.1.2 Count Rate

After assigning values to those variables in the right side of Equation 4, the count is estimated. The PRD has a count rate at 0.46 ± 0.01 cps when it is placed at 1 metre above an inﬁnite plane source that has one kBq/m2 140La deposition.

5.6.1.3 Dose Rate - H*(10)

By assuming γs have a similar energy dependence on the distance for both the exposure and the ground deposition studies, the ambient dose rate is then able to be derived from a convoluted distribution of αhit in Figure 22 and Figure 23 (using the regular distribution rather than the accumulated one). The ambient dose rate calculated this way gives 0.720 x 10−2 µSv/hr.

5.6.2 Geant4 Simulations

In Geant4, a total of 7 x 109 140La decays have been simulated. The simulated sources were uniformly distributed across a disc of 200 m radius. Equivalently, the 140La deposition density on the disc is 55.625 ± 0.001 kBq/m2.

28 DRDC-RDDC-2016-R005 original The γ-rays incident on the PRD, ΦPRD , is found to be 232.00 ± 15.23, while the weighted weighted area weighted count, ΦPRD , is 252.80 ± 15.90. The ratio between ΦPRD and original ΦPRD is 1.09 ± 0.10. Hereafter, this ratio will be used to make conversion between quantities per PRD and quantities per unit area. After applying this ratio on the PRD cross section along its central axis (SPRD), the eﬀective cross section of PRD seen by the disc source becomes:

Seﬀ = 1.09 · SPRD. (7)

5.6.2.1 Fluence Result - Φnorm

weighted By converting ΦPRD from per PRD to a unit area, the ﬂuence Φnorm is derived. As the integrated source disc expands, Φnorm tends to asymptotically approach to 8820.56 ± 766.02 m−2s−1 per kBq/m2 . The uncertainty here is the statistical uncertainty only, which is derived from the ﬁt function. Figure 24 shows the Φnorm result onto a cylindrical PRD from all possible γ-rays. ] •1 104 sec •2 [ m norm Φ

103

Geant4 (Cylinder) All γ contributions

0 50 100 150 200 Distance R2 [m]+1 Figure 24: The fluence calculated from all γ-rays by simulating a cylindrical PRD sitting one metre above a ground which has one kBq/m2 140La deposition on the surface. The red line is the fit function on the fluence, and the dashed line represents the asymptote obtained from the fit. The x-axis is the distance between sensor and source.

primary The primary γ-rays, Φnorm , was estimated to be 5546.90 ± 422.74 (6007.31 ± 632.28) m−2s−1 after integrating over a 200 m (inﬁnite) source disc. It accounts for 68.10 ± 3.06 % of the total ﬂuence.

In addition, the ﬂuence Φnorm was re-estimated by simulating a spherical PRD, which has an isotropically projection area identical to γ-rays from any direction. This

DRDC-RDDC-2016-R005 29 simulation is used to double check the Seﬀ calculation. The sphere simulation leads to primary −2 −1 a Φnorm result of 5561.38 ± 62.68 ( 6571.77 ± 73.19) m s for a 200 m (inﬁnite) source disc. The results agree well with the cylinder results presented in the last paragraph.

5.6.2.2 Count Rate ] 2 Asymptote = 4.922 per kBq/m •1

Total La140 Primary γ s Soil Buildup

Count Rate [ sec Geant4 Simulation 0 50 100 150 200 Distance R2 [m]+1

Figure 25: The simulated “count” rate for a scenario of 1 kBq 140La activity deposition per square metre on the ground. The “count” rate is obtained by assuming the εH and εD are 100%.

Figure 25 shows the accumulated “count” based on simulations with a 200 m disc source. Here the “counts” were obtained before taking the efficiencies εH and εD into account. A fit on the count distribution of all γ-rays (red open circles) gives an asymptote of 4.92 ± 0.43 cps. The separate contributions from the primary and soil-induced γ-rays are also shown in this figure.

After applying the eﬃciencies (εH and εD in Equation 6), the “measured” count rate is obtained and it is 0.837 ± 0.073 cps.

5.6.2.3 Dose Rate - H*(10)

The ambient dose rate was calculated in the same way as discussed in the exposure studies. The H*(10) is estimated to (1.069 ± 0.035) x 10−2 µSv/hr, where the soil buildup contributes 0.105 x 10−2 µSv/hr.

30 DRDC-RDDC-2016-R005 5.6.3 Comparisons and Discussions 5.6.3.1 Fluence Result

Table 4 summarizes the fluence results obtained previously. Based on the original formula used in calculation, the analytical method only considered the air attenuation effect (no buildup effect). Therefore as expected, the analytical result on Φunit (4386.34) is close to the MicroShield calculation without air buildup effect (4574.00).

Table 4: The fluence results calculated from different methods. The Geant4 results include the primary γ-ray contributions only. 200 m disc (m−2s−1) Infinite Plane (m−2s−1) Analytical (Φunit) 4386.34 4479.75 ± 66.93 MicroShield w/o (w/) buildup 4574.00 (5497.00) - Geant4 (Φnorm) - cylinder 5546.90 ± 422.74 6007.31 ± 632.28 Geant4 (Φnorm) - sphere 5561.38 ± 62.68 6571.77 ± 73.19

The air buildup has a large impact on the fluence; for 200 m results, the MicroShield result with air buildup (5497.00) is found to have approximately 20.18% enhancement with respect to the result without buildup effect (4574.00). Besides, the MicroShield result with buildup effect is found to be consistent with the Geant4 sphere and cylinder simulations that included only the primary γ-rays.

The air buildup enhancement for Φ is expected to become larger if the integration is expanded from 200 m to infinity. First, this can be implied from the dependence of the buildup factors [19] on the MFP. Secondly, the Geant4 result reflects this trend well if comparing the two sphere results on Φnorm at different integral ranges. On the other hand, the analytical result Φunit, obtained without air buildup effects, does not seem to change significantly as the integrated radius of the source disc expands.

The soil induced buildup Φ was estimated to be 31.90% of the total Φ in Geant4. By considering all γ-rays, the ﬂuence extrapolated from Geant4 simulation is

8820.56 ± 766.02 m−2s−1 per kBq/m2 ground deposition. (8)

5.6.3.2 Count Rate Table 5: The count rate results from the ground radiation deposition. Count Rate (cps) Analytical method 0.46 ± 0.01 Simulation (primary γs and soil buildup) 0.837 ± 0.073 Simulation (soil buildup) 0.267

DRDC-RDDC-2016-R005 31 The count (rate) results are summarized in Table 5. Since the ground is assumed to have 1 kBq 140La deposition per square metre, then Geant4 implies a conversion factor between the count rate and ground deposition of

0.837 ± 0.073 cps per kBq/m2 deposition of 140La source. (9)

Note that the count rate was estimated by using the detection and hit efficiencies that were estimated for all γ-rays. By using the same εD and εH, the derived count rate results for the analytical result and the soil buildup are therefore only an approximation because these efficiencies normally differ for γs at different energy.

5.6.3.3 Dose Rate

The dose rate results are summarized in Table 6. In Geant4, the soil contributes about 31.9% to the total count rate, however its contribution on dose rate is much lower due to the typical lower energy with respect to these primary 140La γ-rays. The soil buildup only amounts to 9.8% of the overall dose rate. In analogy to this, the air buildup is expected to have a lower inﬂuence too.

Table 6: The dose rate calculations from the ground radiation deposition. Dose Rate (10−2 µSv/hr) Analytical method 0.720 Simulation (primary γs and soil buildup) 1.069 ± 0.035 Simulation (soil buildup) 0.105

5.6.3.4 Conversion Coefﬁcients between Dose Rate and Count Rate

Therefore, the ratio between count rate and dose rate is affected largely by the buildup effects. For the primary γ-rays only, the ratio is estimated to be 59.1 count per µSv/hr, while it goes up to 254.3 per µSv/hr for these soil buildup contributions only. By taking all contributions, the conversion coefficient between count rate and dose rate is

78.3 ± 7.3 cps per µSv/hr. (10)

For the analytical method, a lower conversion factor is expected because the γ-rays used in this method are free of any buildup eﬀects. It gives a conversion factor of 63.9 count per µSv/hr, a slightly lower dose rate than the Geant4 result in Equation 10. However, this result seems to agree with the Geant4 result after neglecting the soil buildup contributions.

32 DRDC-RDDC-2016-R005 6 Conclusions and Future Work 6.1 Conclusions

Monte Carlo simulation, using the Geant4 software package, was used to calculate the response of RadEye PRD detectors (a PRD) to a 140La (ground contamination) in a configuration similar to that used in the FSRDD trials. The γ-ray flux and energy flux were calculated for PRDs in various scenarios. These results were used to calculate the conversion coefficients which allow the estimation of the ambient dose rate and ground deposition levels from the measured count rate in a RadEye PRD.

In general, the ﬂuence from the primary γ-rays can be described by the theoretical calculation after considering the air attenuation and buildup eﬀects. This is the case for both exposure and ground deposition studies. The enhancement due to the air buildup is about 20% for the ground contamination case. The presence of the soil provides extra γs exposure to sensor, and its contribution is found to be around 25% to 40% for both studies.

Because these buildup γs have lower energy, their contribution to H*(10) is usually smaller than what is expected from the Φ result. The soil buildup was found to contribute only about 10% to the total H*(10) (see Figure 1 for more understanding). Therefore, the ratio Φ/H*(10) is usually greater when the buildup γs are included, compared to the ratio that is estimated only from the primary γs.

However, the useful conversion factor in practice is from the count rate to dose rate or vice versa. Therefore, it is critical to estimate the εH and εD so as to translate Φ to count rate. As discussed previously, various factors exist to influence these efficiencies. For example, the source type and sensor type/geometry matter for εH and the energy threshold affects εD. These factors imply that one conversion factor obtained from one scenario may not applicable to another.

6.1.1 Soil Buildup Effect

The soil buildup effect was found to have an important impact on the total γ-ray flux, energy flux and the average energy of the radiation hitting the PRDs. The significance of the effect varies, depending on the distance between sensor and source. It leads to a γ-ray flux increase of 25% at 10 m and up to ∼40% for greater distances. The impact of the buildup reached equilibrium for the PRDs located 100 m or farther from the source. This can be partially explained by the air attenuation effect on the primary radiation from the 140La at those distances (Mean Free Path and Half Value Length). However, the behaviour of the buildup contribution will require further study to understand in its entirety. For the primary γ-rays from the source, the flux follows the inverse square law well, after taking air attenuation/buildup into account.

DRDC-RDDC-2016-R005 33 In analog to the soil buildup effect, the presence of other materials introduces additional changes to the γ-ray flux and energy spectrum at the detector locations. These materials could be either beside the source or sensor, or in (or near) the path between them. Furthermore, the coefficients for converting the measured count rate to dose rate could vary greatly for different buildup scenarios. In order to correctly convert from a measured count rate to a derived dose rate in a real-life scenario, one must take into account the buildup effect on the γ-ray flux and energy spectrum.

6.1.2 Ambient Dose Rate Estimation

The dose rate was determined by calculating the weighted fluence from each individual γ-ray. The H*(10) results vary largely for PRDs at different distances, from about 4 µSv/hr at 10 m down to 0.001 µSv/hr at 400 m. The soil buildup only contributes about 11.2% to the total H*(10) due to the typically low energy that the scattered γs have. For the same reason, the γ-rays arising from the other buildup effects are expected to have a lower H*(10) contribution than their contribution to Φ.

6.1.3 Count Rate Efﬁciency and Conversion to Dose Rate

The ratios between the hit ﬂux and dose rate were found to increase as distance increases for the source/detector geometry used in the Geant4 simulation. This distance-dependence is due to scattering of the emitted 140La γ-rays. Scattering causes the γ spectrum to shift to lower and lower energy as the distance increases, meaning that the amount of deposited energy (dose rate) decreases faster than the photon ﬂux because there is less energy per photon at large distances.

After implementing the energy threshold at 120 keV and applying the detection efficiency εD, the factors required to convert the count rate to dose rate were found to be flat across the distance range. The energy cut essentially reduces the lower energy γs (mostly due to the buildup effects) to a certain extent, and results in a roughly constant distribution for the conversion factors. The count-rate-to-dose-rate conversion coefficient was determined to be at the range of 60 to 70 cps/(µSv/hr), as shown in Figure 22.

The overall count-rate detection efficiency (measured counts per simulated hit γ) for the RadEye PRD was estimated to be 35.9% by comparing the Geant4 simulation results to the data collected at the FSRDD trials (Figure 17). After apply an energy threshold (120 keV) on the hit flux, the count-rate efficiency distribution was relatively flat (Figure 20) over the range of distances at which the RadEye PRDs were placed in the Geant4 simulation (and in the FSRDD trials). This 59.1% count rate efficiency should therefore be broadly applicable to the FSRDD measurements.

34 DRDC-RDDC-2016-R005 6.1.4 Ground Deposition

The FSRDD trials used explosives to disperse a 140La radioactive source over a wide area, and used (among other systems) an array of RadEye PRDs to measure the count rate due to the resulting contamination. Data from the pre-explosion conﬁguration were used to compare to model results in order to determine the PRD count-rate eﬃciency and the conversion factors from count rate to dose rate.

These results were applied to the post-explosion configuration in which the PRDs were used to take count rate measurements in the middle of a contaminated field. Conversion factors were then determined to take the PRD count-rate measurements and use them to estimate the ground-shine dose rate and the radioactive contamination concentration on the ground at the location of the PRD. These conversion factors were calculated using three methods, all of which made use of the approximation that the PRD was 1 m above an infinite plane of uniform contamination. The first was a simple mathematical model, the second was a detailed Geant4 Monte Carlo calculation, and the third involved the convolution of the dose rate conversion factor as a function of distance (Figure 22) with the fluence as a function of distance (Figure 23).

The PRD sensor has a tiny dimension, therefore the count was found to be low; Geant4 calculations determined a count rate of 0.837 ± 0.073 cps for an inﬁnite plane which is contaminated at 1 kBq m−2. Because the energy of secondary, or buildup, γ-rays is generally lower than the primary γ-rays, its contribution to H*(10) is expected to be lower. The buildup γ-rays contribute about 10% to H*(10) while they contribute about 30% to the total ﬂuence. Geant4 predicted that a measured count rate 78.3 ± 7.3 cps is equivalent to 1 µSv/hr. To a good approximation, the analytical method seems to agree with the H*(10) result that was from the primary γs only.

6.2 Future Work The Geant4 results obtained in this work can be used to understand the response of the RadEye PRDs in scenarios with a point source and in a contaminated areas. These are speciﬁcally applicable to the analysis of the RadEye PRD array results from the FSRDD trials, but can be generalized to other applications of the PRDs. There are, however, some details and results that require more simulation studies or analysis to be better understood: • The soil buildup is not well understood and requires a theoretical explanation or more simulations to understand the details of its dependence on distance. • The contribution from naturally occurring background radiation should be simulated and estimated in the future.

DRDC-RDDC-2016-R005 35 • More dedicated studies are needed to estimate εH and εD with improved modelling on RDDs, especially for the buildup γ-rays.

36 DRDC-RDDC-2016-R005 References

[1] D. S. Haslip, M. R. Desrosiers, L. S. Erhardt and C. L. Larsson, Radiological Terrorism Feasibility Assessment, DRDC Ottawa, TM 2006-103, May (2006).

[2] D. S. Haslip, C. L. Larsson and L. S. Erhardt, Development of Radiological Terrorism Scenarios, DRDC Ottawa, TM 2006-113, June (2006).

[3] D. Waller and H. S. Haslip, An evaluation of the probabilistic risk assessment tool for radiological dispersal devices, DRDC Ottawa, TM 2009-274, (2009).

[4] C. Liu and D. Waller, The Probabilistic Risk Assessment Tool For Radiological Dispersal Devices, DRDC Ottawa, TM 2013-126, December (2013).

[5] L. Erhardt, D. Quayle and S. Noel, Completion report for CRTI 07-0103RD, DRDC Ottawa, TR 2013-056, October (2013).

[6] ICRU(1985). International Commission on Radiation Units and Measurements, Determination of Dose Equivalents Resulting from External Radiation Sources, ICRU Report 39.

[7] ICRU(1988). International Commission on Radiation Units and Measurements, Determination of Dose Equivalents Resulting from External Radiation Sources–Part2, ICRU Report 43.

[8] ICRU(1992). International Commission on Radiation Units and Measurements, Measurement of Dose Equivalents from External Photon and Electron Radiations, ICRU Report 47.

[9] Mats Isaksson, Environmental Dosimetry–Measurements and Calculations, Radioisotopes–Applications in Physical Sciences, Prof. Nirmal Singh (Ed.), ISBN: 978-953-307-510-5, InTech, DOI: 10.5772/22731. (2011).

[10] http://www.thermoscientiﬁc.com/en/product/radeye-prd-prd-er-personal- radiation-detector.html.

[11] S. Agostinelli etc., Geant4–a simulation toolkit, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 506, Issue 3, Pages 250–303, July 2003.

[12] J. Allison etc., Geant4 developments and applications, Nuclear Science, IEEE Transactions on Volume 53, Pages 270–278, Feb 2006.

[13] C. Liu, Installation of GEANT4 toolkit at CARDS computer cluster, DRDC Ottawa, CR 2012-178, Oct 2012.

DRDC-RDDC-2016-R005 37 [14] J. Tuli, Evaluated Nuclear Structure Data File (ENSDF), http://www.nndc.bnl.gov/ensdf/, Dec. 2010.

[15] A. S. Hoover, Simulation and Modeling for the Stand-Oﬀ Radiation Detection System (SORDS) using GEANT4, IEEE Nuclear Science Symposium, 2009.

[16] N. Kucuk etc., Determining photon energy absorption parameters for diﬀerent soil samples, J Radiat Res, 54(3): 578–586, May 2013.

[17] The attenuation coeﬃcient database in National Institute of Standards and Technology. Webpage http://www.nist.gov/pml/data/xraycoef/.

[18] http://www.ans.org/store/vi-240180/.

[19] ANSI/ANS-6.4.3, American Nuclear Society, Gamma ray attenuation coeﬃcient and buildup factors for engineering materials, 1991.

38 DRDC-RDDC-2016-R005 List of Acronyms

cps Count Rate per Second DRDC Defence Research and Development Canada ENSDF Evaluated Nuclear Structure Data File FSRDD Full Scale RDD Experiments and Models GBq Giga Becquerel GEANT GEometry ANd Tracking HVL Half Value Length ICRU International Commission on Radiation Units & Measurements MFP Mean Free Path NaI Sodium Iodide NIST National Institude of Standards and Technology PMT Photo-Multiplier Tube PRD RadEye Personal Radiation Detector RDD Radiological Dispersal Device RSA RadEye Sensor Array

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40 DRDC-RDDC-2016-R005 DOCUMENT CONTROL DATA (Security markings for the title, abstract and indexing annotation must be entered when the document is Classiﬁed or Designated.) 1. ORIGINATOR (The name and address of the organization preparing the 2a. SECURITY MARKING (Overall security document. Organizations for whom the document was prepared, e.g. Centre marking of the document, including sponsoring a contractor’s report, or tasking agency, are entered in section 8.) supplemental markings if applicable.) DRDC – Ottawa Research Centre UNCLASSIFIED 3701 Carling Avenue, Ottawa ON K1A 0Z4, Canada 2b. CONTROLLED GOODS (NON-CONTROLLED GOODS) DMC A REVIEW: GCEC DECEMBER 2012 3. TITLE (The complete document title as indicated on the title page. Its classiﬁcation should be indicated by the appropriate abbreviation (S, C or U) in parentheses after the title.) GEANT4 Simulation of the RadEye Response to Point Source and Ground Deposition for the Full-Scale Radiological Dispersal Device Field Trials

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5. DATE OF PUBLICATION (Month and year of publication of 6a. NO. OF PAGES (Total 6b. NO. OF REFS (Total document.) containing information. cited in document.) Include Annexes, Appendices, etc.) January 2016 54 19

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Radiological Dispersal Device (RDD) characterization is an area of active research for security and military purposes. Defence Research and Development Canada (DRDC) has been conduct- ing research in this area since 2003. The evaluation of RDD consequences, however, is difficult to do in practice due to practical and safety issues around the use of radioactive isotopes that are considered threats in RDDs. At present, theoretical models are heavily relied upon to characterize and describe RDD dynamics, and to derive useful quantities such as airborne concentration and ground deposition. In order to build up experimental data on real-world RDD effects, DRDC Ottawa Research Centre led a series of Full-Scale RDD (FSRDD) trials in 2012 that used a short-lived radioactive source (140La) as a surrogate for threat isotopes. In this work, the count rate, energy spectrum, and dose rate were calculated using Geant4 simulations for the gamma sensor array that was used in trials. The buildup effect due to gamma- ray scattering in the soil was explored in detail, and the contribution proved to be significant in this case. The ambient dose rate was derived from the γ-ray flux results. Conversion coefficients were determined, allowing the calculation of dose rate and the ground deposition from count rate measurements.

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Monte Carlo Simulation GEANT4 NaI Simulation Soil Simulation RDD (Radiological Dispersal Device) Full Scale RDD Trials RadEye Sensor Ambient Dose Rate Conversion Factors Ground Radiation Deposition www.drdc-rddc.gc.ca