Simulation of gamma spectra from soil samples by using MCNP: A case study
Main Article Content
Abstract
A comparison between the MCNP simulated gamma spectrums based on the nuclear data from NuDat with the current version 3.0 and Nucléide-Lara against measured spectra based on the IAEA reference samples has been performed to assess the influence of nuclear data of photon decay processes and cross-section of photon interactions on the quality of the simulated spectra. As the results, the appearance of some abnormal energy peaks; namely 215 keV, 571 keV, 675 keV, 1227 keV of the NuDat-based simulated spectra and 90 keV, 94 keV, 106 keV, 416 keV of the Nucléide-Lara-based simulated spectra, which were present in neither the measured spectrum nor remaining simulated spectra, indicating issues with accuracy and completeness of these dataset. In addition, the good correlation between the combined dataset-based simulated spectra and reference samples-based measured spectra within the range of 50 keV to 2620 keV suggests that this MCNP simulation configuration can be used to generate a large simulated dataset for Machine Learning (ML) models that automatically identify and qualify radioactive isotope from gamma-ray spectra, overcoming the practical limitation of number of reference samples to sufficiently generate data for training and testing ML algorithms in the field of environmental radiation [1].
Article Details
Keywords
gamma spectra, soil samples, MCNP, simulation, NuDat, Nucléide-Lara
References
[2]. Gauld, I. C. et al. Quantifying Nuclear Data Uncertainties in National Security Applications.
[3]. National Nuclear Data Center. NuDat 3.0. NuDat 3.0.
[4]. Laboratoire National Henri Becquerel. Nucléide-Lara. Nucléide-Lara.
[5]. Conti, C. C., Salinas, I. C. P. & Zylberberg, H. A detailed procedure to simulate an HPGe detector with MCNP5. Progress in Nuclear Energy 66, 35–40 (2013).
[6]. Hendricks, J. S., Swinhoe, M. T. & Favalli, A. Monte Carlo N-Particle Simulations for Nuclear Detection and Safeguards: An Examples-Based Guide for Students and Practitioners. (Springer International Publishing, 2022). doi:10.1007/978-3-031-04129-
[7]. Elanique, A. et al. Dead layer thickness characterization of an HPGe detector by measurements and Monte Carlo simulations. Applied Radiation and Isotopes 70, 538–542 (2012).
[8]. Chuong, H. D., Thanh, T. T., Ngoc Trang, L. T., Nguyen, V. H. & Tao, C. V. Estimating thickness of the inner dead-layer of n-type HPGe detector. Applied Radiation and Isotopes 116, 174–177 (2016).
[9]. Huy, N. Q., Binh, D. Q. & An, V. X. Study on the increase of inactive germanium layer in a high-purity germanium detector after a long time operation applying MCNP code. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 573, 384–388 (2007).
[10]. Loan, T. T. H., Ba, V. N., Thy, T. H. N., Hong, H. T. Y. & Huy, N. Q. Determination of the dead-layer thickness for both p- and n-type HPGe detectors using the two-line method. J Radioanal Nucl Chem 315, 95–101 (2018).
[11]. Mezerreg, N., Azbouche, A. & Haddad, M. Study of coincidence summing effect using Monte Carlo simulation to improve large samples measurement for environmental applications. Journal of Environmental Radioactivity 232, 106573 (2021).
[12]. William Johnson. InterSpec gamma radiation analysis software. (2018).