### Introduction

### Materials and Methods

### 1. Experimental measurement apparatus

_{2}reflector, with the exception of the light output surface on the photodiode. The silicon pin photodiode was S1223-013), developed by Hamamatsu (Hamamatsu, Japan). It has an active area of 3.6×3.6 mm

^{2}. The A2504) preamplifier was developed by Amptek (Bedford, MA), and the printed circuit board for the compact radiation sensor and the preamplifier was Amptek’s PC2505). The shaping amplifier was a Spectroscopy Amplifier 672, Ortec6)(Oak Ridge, TN). The MCA was PCI Trump Multichannel Buffer, Ortec, which has 2048 channels of spectral bin. Finally, the data acquisition software used was MAESTRO, Ortec.

### 2. Gamma energy identifying algorithm

##### (1)

*i*is the order of each magnitude from 20 keV to 1,500 keV in the magnitude set.

*E*

*is the original gamma energy, and*

_{O}*E*

*is the expected gamma energy calculated by the original spectral decomposition or the spectral decomposition with the smoothing method.*

_{e}### 3. MCNP simulation

^{241}Am), 5.1% of 0.662 MeV (

^{137}Cs), and 1.6% of 1.33 MeV (

^{60}Co). These energy resolutions are common values using a CsI (Tl) scintillator [8]. The equation to set the FWHM in MCNP is expressed as Equation 4,

_{2}reflector. The simulated energy spectra from 20 keV to 1,500 keV are shown in Figure 1. The X-axis is gamma energy, the Y-axis is spectral bin energy, and the Z-axis is absolute response probability.

### Results and Discussion

^{137}Cs source is shown in Figure 2. The maximum magnitude was 660 keV of the gamma energy on the x-axis, and the other magnitudes of around 660 keV in the energy range of the FWHM (30 keV) were distributed with positive values. The magnitudes of modeling error are distributed all over the interested gamma energy range. The enhanced magnitude set with the smoothing method is shown in Figure 3. The standard deviation of the magnitude set of the spectral decomposition with smoothing method decreased by 35.8% when compared with the magnitude set of the original spectral decomposition, while the added magnitude indicating the 660 keV changed very little.

^{133}Ba, the average error of 80 keV was close to zero, but the average error of 356 keV was relatively higher than the average error of the other gamma energies. The energy spectra of 356 keV gamma rays was hard to classify with the other radiation from

^{133}Ba which has similar energy as 384 keV. The average error of

^{60}Co was also higher than the lower energies, however 1,170 keV and 1,330 keV were not hardly interfered between each other than the 356 keV and 384 keV. The average error of

^{60}Co was caused by the low mass attenuation coefficients (1,170 keV (0.051 cm

^{2}·

*g*

^{−1}) and 1,330 keV (0.048 cm

^{2}·

*g*

^{−1}) [9]. The average error caused by these two parameters decreased in the whole energy range after applying the smoothing method—it was reduced to zero with

^{241}Am,

^{57}Co,

^{133}Ba,

^{137}Cs and

^{54}Mn and decreased by half with

^{22}Na and

^{60}Co.

^{241}Am and

^{57}Co were equally identified by only 1 calculation based on the spectral decomposition (black circles) and the spectral decomposition with smoothing method (red crosses). In the case of

^{137}Cs and

^{54}Mn, which have higher gamma radiation energy than

^{241}Am and

^{57}Co, the number of repeated calculations increased in the low fluence condition, but the spectral decomposition with smoothing method had a smaller number of repeated calculations than the original spectral decomposition. The number of calculations for the source which emits multiple energy gamma radiations such as

^{133}Ba,

^{22}Na and

^{60}Co increased in comparison with other radio isotopes because of the mass attenuation coefficient and the ratio of spectral count between the two energies of gamma rays. Thus a larger number of calculations were needed to identify the two dominant gamma energies. But the number of repeated calculations for these sources also decreased by half after adapting the smoothing method. This means that the performances of the energy identifying algorithm were enhanced by reducing the magnitude of modeling error even in low fluence conditions.

### Conclusion

^{4}(0.09 cm

^{−2}). Through the development of this algorithm, we have confirmed the possibility of developing a product that can identify artificial radionuclides nearby using radiation sensors that are easy to use by the public. Therefore, it can contribute to reduce the anxiety of the public exposure by determining the presence of artificial radionuclides in the vicinity.