Search

Methodology for suppressing or recovering the distorted spectra, which may occur due to mutual non-uniformity and nonlinear response when a multi-detector is simultaneously operated for gamma spectroscopy, is presented with respect to its applicability to stabilization of spectra having the non-identical feature using modified full spectrum reallocation method. The modified full-spectrum reallocation method is extended to provide multiple coefficients that describe the gain drift for multi-division of the spectrum and they were incorporated into an optimization process utilizing a random sampling algorithm. Significant performance improvements were observed with the use of multiple coefficients for solving partial peak dislocation. In this study, our achievements to confirm the stabilization of spectrum having differences in moments and modify the full spectrum reallocation method provide the feasibility of the method and ways to minimize the implication of the non-linear responses normally associated with inherent characteristics of the detector system. We believe that this study will not only simplify the calibration process by using an identical response curve but will also contribute to simplifying data pre-processing for various studies as all spectra can be stabilized with identical channel widths and numbers.

The detector suffers from pulse pileup by overlapping of the signals when it was used in high radiation fields. The pulse pileup deteriorates the energy spectrum and causes count losses due to random coincidences, which might not resolve within the resolving time of the detection system. In this study, it is aimed to propose a new pulse pileup correction method. The proposed method is to correct the start point of the pileup pulse. The parameters are obtained from the fitted exponential curve using the peak point of the previous pulse and the start point of the pileup pulse. The amplitude at the corrected start point of the pileup pulse can be estimated by the peak time of the pileup pulse. The system is composed of a NaI (Tl) scintillation crystal, a photomultiplier tube, and an oscilloscope. A 61 μCi 137Cs check-source was placed at a distance of 3 cm, 5 cm, and 10 cm, respectively. The gamma energy spectra for the radioisotope of 137Cs were obtained to verify the proposed method. As a result, the correction of the pulse pileup through the proposed method shows a remarkable improvement of FWHM at 662 keV by 29, 39, and 7%, respectively.

With advancements in machine learning technologies, artificial neural networks (ANNs) are being widely used to improve the performance of gamma-ray spectroscopy based on NaI(Tl) scintillation detectors. Typically, the performance of ANNs is evaluated using test datasets composed of actual spectra. However, the generation of such test datasets encompassing a wide range of actual spectra representing various scenarios often proves inefficient and time-consuming. Thus, instead of measuring actual spectra, we generated virtual spectrawith diverse spectral features by sampling from categorical distribution functions derived from the base spectra of six radioactive isotopes: 54Mn, 57Co, 60Co, 134Cs, 137Cs, and 241Am. For practical applications, we determined the optimum counting time (OCT) as the point at which the change in the Kullback–Leiblerdivergence (ΔKLDV) values between the synthetic spectra used for training the ANN and the virtual spectra approaches zero. The accuracies of the actual spectra were significantly improved when measured up to their respective OCTs. The outcomes demonstrated that the proposed method can effectively determine the OCTs for gamma-ray spectroscopy based on ANNs without the need to measure actual spectra.