ISOCS Characterization of Sodium Iodide Detectors for Gamma-Ray Spectrometry

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1 ISOCS Characterization of Sodium Iodide Detectors for Gamma-Ray Spectrometry Sasha A. Philips, Frazier Bronson, Ram Venkataraman, Brian M. Young Abstract--Activity measurements require knowledge of the detector efficiency for the specific measurement geometry. Often it is not possible to perform a source-based efficiency calibration for the geometry of interest. In such cases the calculation of a response function or a Monte Carlo simulation for the detector may be used. Such methods can be time prohibitive, however, particularly in the case of measurement situations requiring a variety of counting geometries. The ISOCS methodology provides a pre-existing detector response which already contains the Monte Carlo calculated detector efficiencies for points in all spatial directions over a wide range of energies. The efficiency for a particular counting geometry can then be calculated very rapidly for a variety of source distributions and measurement geometries. The ISOCS software was traditionally limited to use with germanium (Ge) detectors, but recently has been extended to use with sodium iodide (NaI) detectors as well. This work presents the results of using a Monte Carlo simulation to characterize the efficiency response of 3 x3 and 2 x2 NaI detectors at several energies. The results from the ISOCS calculation were compared with measurements demonstrating the level of agreement for point and distributed sources. In addition the difficulties encountered in using a multi-line spectrum to determine the accuracy of the mathematical efficiency calibration were studied and these results are presented as well. I. INTRODUCTION CTIVITY measurements necessarily require a detector Aefficiency calibration for the specific measurement geometry. Since it is often not possible to perform a sourcebased efficiency calibration for the geometry of interest, there is a need for alternative methods of obtaining efficiency calibrations. While detector responses can be calculated or simulated using Monte Carlo methods, in counting situations with a variety of measurement geometries these methods must be readily adaptable to provide accurate results in a reasonable time frame. Canberra Industries, Inc., (Canberra) previously developed the ISOCS [1] methodology for Ge detectors, where a preexisting detector response grid can be obtained with a given detector. The grid contains Monte Carlo calculated detector Manuscript received November 1, S. A. Philips is with Canberra Industries, Inc., Meriden, CT USA (telephone: , sphilips@canberra.com). F. Bronson is with Canberra Industries, Inc., Meriden, CT USA (telephone: , fbronson@canberra.com). R. Venkataraman is with Canberra Industries, Inc., Meriden, CT USA (telephone: , rvenkataraman@canberra.com). B.M. Young is with Canberra Industries, Inc., Meriden, CT USA (telephone: , byoung@canberra.com). efficiencies for points in all spatial directions over a wide range of energies, allowing the rapid determination of the efficiency for a variety of source distributions and measurement geometries. Recently, there has been a need for such a tool in the use of NaI detectors as well, particularly in the case of hand-held devices. The ISOCS methodology was thus extended for use with 3 x3 (76mm length x 76mm diameter) and 2 x2 (51mm length x 51mm diameter) NaI detectors. Presented here are the results from the ISOCS calculations compared with measurements for point and distributed sources demonstrating the overall level of agreement. II. OVERVIEW OF THE ISOCS METHODOLOGY The process of developing an efficiency response grid for a detector in the ISOCS methodology is referred to as the detector characterization and is briefly summarized here. The first step in a characterization is the development and validation of a Monte Carlo model for the particular detector. This is based on the comparison of measured efficiencies to calculated efficiencies for a few specific measurement geometries. The measured efficiencies are determined using a certified source containing a single nuclide with several γ- lines, or a multi-nuclide source so as to span a wide range of energies. The calculated efficiencies are determined from a Monte Carlo model using MCNP [2]. Once the model has been validated it is used in the generation of a large number of efficiency data sets for different points in space in effect providing a detector response to point-like sources at many locations about the detector. The response map, or grid, is then stored in a characterization file that is supplied with the application software. When the efficiency response is required for a particular counting geometry, the software can be used to model the counting geometry as well as to define the source using a variety of source shapes and distributions. The source is subdivided into several small volume elements (voxels) and the efficiency is calculated for each voxel. Attenuators are taken into account by using a multiple pathway approach, and an averaging procedure is used to obtain the final efficiency for the counting geometry.

2 III. DEVELOPMENT AND VALIDATION OF THE MCNP MODEL MCNP models for both the 3 x3 and 2 x2 NaI detectors were developed based on the detector dimensions provided by the manufacturer. Dimensions such as the length and diameter of the crystal, the thickness of the reflective oxide layer, and the endcap dimensions were accurately reproduced in developing the models. In addition, the material behind each crystal was modeled to more accurately represent possible back-scatter. This includes the light guide (when present) and the photo-multiplier tube, both of which are enclosed by the endcap. A limitation of the model is that MCNP does not simulate any of the light properties of scintillators. Uncertainties can thus stem from the creation, transport, and collection of light in the detector system. The light response of a NaI crystal is known to be a function of energy deposited and crystal temperature. Light transport in the detector is influenced by the reflective oxide coating and the possible existence of slight imperfections in the crystal. Also the behavior of the photomultiplier tube in converting light into signal can vary depending on several factors including temperature. To refine and validate the detector models, the MCNP efficiencies for four different source geometries were compared against the corresponding measured efficiency values. A specially built source-positioning jig was used to locate the sources at a fixed distance and angle ensuring accurate reproducibility of the measurement geometry. The jig consists of a radial arm with a source holder located at the end of the arm. The radial arm can be rotated about a pivotal point and can be fixed at specific angles including 0 and 90 degrees. The measurements were made inside a Canberra Q 2 shield in order to reduce external background. The detector, jig, and shield are shown in Fig. 1. Every effort was made to ensure stable temperature conditions during the measurements. The source geometries used in the validation process were as follows. Point source on-axis approximately 30 cm from the endcap face. Point source at 90 degrees, 1.5 cm below the endcap face and approximately 32 cm away from the axis of NaI crystal. Mixed-γ vial source on-axis approximately 28 cm from the endcap face. Mixed-γ vial source at 90 degrees, 1.5 cm below the endcap face and approximately 30 cm away from the axis of NaI crystal. A. Point Source Measurements The point source used in the validation measurements was a 137 Cs button source that was cross calibrated against NIST standard reference material. The active portion of the source is a bead 3 mm in diameter deposited in a plastic button matrix. The center of the bead is approximately 0.5 mm from the front face of the button. The activity was determined with a 1sd uncertainty of 3.29%. The results of the comparison between MCNP and measurement are shown in Table 1, where the first row gives the results for the 3 x3 and the second row for the 2 x2 detector. The results of the point source measurements show that the MCNP values at 662 kev were within 5-10% of the measured values. TABLE 1 COMPARISON BETWEEN MCNP AND MEASUREMENT FOR POINT SOURCE GEOMETRIES AT 0 AND 90 DEGREES Fig x3 NaI detector in jig placed in Canberra Q 2 shield. B. Mixed-γ Source Measurements The Mixed-γ vial source was a standard mixed gamma source containing the following nuclides: 109 Cd, 57 Co, 139 Ce, 203 Hg, 113 Sn, 137 Cs, 60 Co, and 88 Y. The active portion of the source is a solid cylindrical matrix of density 1.15 g/cm 3 with an approximate volume of 22 ml. The photons of interest had energies of 88 kev ( 109 Cd), 122 kev ( 57 Co), 166 kev( 139 Ce), 279 kev ( 203 Hg), 392 kev ( 113 Sn), 662 kev ( 137 Cs), 898 & 1836 kev ( 88 Y), and 1173 & 1332 kev ( 60 Co). For measurements made with a mixed-γ vial source additional uncertainties can be introduced due to the use of a distributed source as well as due to the difficulties in the calculation of the correct net peak areas for a NaI spectrum

3 with multiple lines. Unlike the case of high resolution Ge detectors, measurements with NaI detectors give rise to spectra with broad peaks for which the peak area determination can be less accurate. This is predominantly related to the estimation of the continuum under the peak when using standard continuum subtraction algorithms. This issue must be addressed in order to obtain a true measure of the accuracy of the MCNP model when compared with measurements. IV. PEAK-AREA ESTIMATION FOR OVERLAPPING PEAKS One example of the difficulty encountered with overlapping peaks in a multi-line NaI spectrum is when the centroid of one peak is in the Compton gap of another peak. The Compton gap is the region of the spectrum between the full-energy peak and the Compton edge. The resolution-smeared Compton gap has an exponential shape (concave-like) which falls under the standard step-background shape. Here the peak area estimation will be too low since a standard step-background shape is an overestimation of the actual background. The Compton gap problem occurs when the problematic peak is lower in energy than the interfering peak, but higher than its Compton edge. The 1173 kev peak from 60 Co is such a case where the interfering peak is the 1332 kev peak also from 60 Co. In fact in the case of 60 Co the two peaks are very close to each other so as to overlap which also leads to a difficulty in the estimation of the area of the 1332 kev peak when using standard background algorithms. A second case arises when one peak sits atop the Compton edge of another peak. The Compton edge results in a knee - like shape which rises above the standard step-background shape. Here the peak area estimation will be too high since the step-background algorithm is an underestimation of the actual background. The Compton edge of the 88 Y 898 kev peak is an example of this situation, affecting the peak area determination of the 137 Cs 662 kev peak. The broad peaks in NaI spectra can also mask the presence of nearby escape peaks which will also lead to the incorrect determination of a full-energy peak efficiency. For the combination of nuclides used in the Mixed-γ source, the single escape peak at 1325 kev from the 88 Y 1836 kev photon was found to affect the accurate determination of the peak area for the 60 Co 1332 kev peak. Estimating the true peak area in these cases can be done in one of two ways. The standard peak-area algorithms can be abandoned in favor of a method whereby each peak in the spectrum is analyzed by forcing a fit with the correct (known) background shape. This form of spectrum deconvolution can be cumbersome since the background shape can vary from peak to peak. An alternative method is to simply use the standard peak-area algorithms and estimate a correction factor on a peak by peak basis. This is the approach that was taken to accurately estimate the level of agreement between the MCNP model and the Mixed-γ source measurements. V. CORRECTION FACTORS Correction factors were obtained by using the peak analysis software to analyze MCNP generated spectra for the peaks in question. In the case of the Compton gap problem described previously, first, MCNP was used to establish the true efficiency for the 1173 and 1332 kev photons separately. Here the true efficiency is the full-energy peak efficiency obtained at the energy of interest. Next, an energy-broadened MCNP spectrum was generated for each photon, again separately, and the spectra were analyzed using the peak analysis software. The resulting efficiencies were indistinguishable from the true efficiencies showing that for ordinary peak shapes the peak analysis software provided accurate results. Finally, an MCNP spectrum was generated for both photons simultaneously and again analyzed using the same peak analysis software. The results were found to be different based solely on the continuum subtraction used in the peak analysis. Fig. 2 shows an overlay of the two individual MCNP peak spectra and the summed spectrum of the two peaks, illustrating the actual shape of the continuum in the summed spectrum. Fig. 2. MCNP spectra for 60 Co individual and summed 1173 kev and 1332 kev peaks kev spectrum indicated with + symbol; 1332 kev spectrum indicated with x symbol; summed spectrum indicated by solid line. Fig. 3. MCNP summed spectrum for 60 Co analyzed with peak-analysis software.

4 Fig. 3 shows the result of analyzing the summed spectrum using the peak analysis software where it is seen that the assumed continuum shape is clearly quite different from the actual continuum shape seen in Fig. 2. The difference in peak areas obtained solely from the peak analysis method is used to obtain the correction factor and is applied in the determination of the final efficiency value. Similar analyses were carried out for the case of the Compton edge problem and for the estimation of correction factors for escape peaks. Table 2 shows the result of implementing the correction factors when comparing MCNP to measured efficiencies for the 3 x3 detector in the on-axis geometry. No uncertainties were assigned to the correction factors themselves. background shapes here are also not well known and not well represented by the step background. In addition at the very lowest energy there is possible interference from X-rays, particularly in the case of the X-ray emitted in the decay of 203 Hg or from the lead shielding which can interfere with the peak at 88 kev. It is difficult to obtain consistent correction factors in this region and so none were applied. TABLE 4 COMPARISON OF MCNP EFFICIENCY AND MEASURED EFFICIENCY FOR 2 X2 TABLE 2 RESULT OF APPLYING CORRECTION FACTORS TO PEAKS OF INTEREST IN A MULTI-LINE NAI SPECTRUM VI. MIXED-γ SOURCE MEASUREMENT RESULTS Following the implementation of correction factors for the peak-analysis effects discussed above, the results of the comparison of MCNP and measurement over a wide range of energies are shown in Tables 3 and 4. TABLE 3 COMPARISON OF MCNP EFFICIENCY AND MEASURED EFFICIENCY FOR 3 X3 VII. ISOCS RESULTS Based on the results above the MCNP models were used to create detector response maps for the 3 x3 and 2 x2 NaI detectors to be used with the ISOCS software. Tables 5 and 6 TABLE 5 COMPARISON OF ISOCS EFFICIENCY AND MEASURED EFFICIENCY FOR 3 X3 In general at low energies where the peaks of interest are closer together and have more overlap, the discrepancy between the model and measurement tends to be worse. The

5 TABLE 6 COMPARISON OF ISOCS EFFICIENCY AND MEASURED EFFICIENCY FOR 2 X2 TABLE 7 QUOTED ISOCS UNCERTAINTIES FOR 3 X3 AND 2 X2 NAI DETECTORS COMPARED WITH GERMANIUM DETECTORS show the result of using the ISOCS software to calculate the efficiencies for the mixed-γ source measurement geometries used in the validation process. This is a final check to ensure that the ISOCS results are indeed consistent with measured efficiencies. The results are seen to be consistent with the results in Tables 3 and 4 as expected. The uncertainties in the ISOCS calculated efficiencies are assigned conservatively. They are attributed to a large degree to the possible difficulties in peak area determinations in NaI spectra, rather than the MCNP simulations or the ISOCS methodology itself. The quoted uncertainties in ISOCS calculated efficiencies for NaI detectors are summarized in Table 7 and compared with those quoted for Ge detectors. VIII. CONCLUSION The ISOCS software can now be used in applications involving 3 x3 and 2 x2 sodium iodide detectors as well as germanium detectors. While the declared ISOCS uncertainties are larger than those for germanium detectors this can be attributed primarily to the difficulties encountered in analyzing sodium iodide spectra. An advantage over germanium detectors, however, is that the ISOCS response grid for a given sodium iodide crystal size can be applied to other sodium iodide detectors of the same size. Additionally, this work addresses some of the inherent limitations to sodium iodide spectroscopy that reduce the accuracy of source based efficiency determinations. IX. REFERENCES [1] R. Venkataraman, F. Bronson, V. Atrashkevich, B.M. Young, and M. Field, Validation of In-situ Object Counting System (ISOCS) Mathematical Efficiency Calibration Software, Nucl. Instrum. Methods Phys. Res. A422, 1999, pp [2] J.F. Breismeister (Ed.), MCNP-A general Monte Carlo N particle Transport Code Version 4B, Los Alamos National Laboratory Report LA M, Los Alamos, 1997.

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