The development of AI-driven radiation detection systems is hindered by the lack of high-fidelity data representing complex shielding scenarios. Most existing datasets rely on simplified exponential attenuation models, ignoring stochastic scattering effects. This study challenges this approach by presenting a Geant4-based high-fidelity simulation of gamma-ray transport for Special Nuclear Materials (239Pu). While high-Z shielding (Lead) demonstrated predictable exponential attenuation (μ-validation error < 1.2%), our results reveal a critical anomaly in low-Z shielding (Polyethylene). We observed a significant non-monotonic "buildup effect" where the detected photon intensity increases by
Keywords
Monte Carlo SimulationGeant4Radiation ShieldingBuildup FactorNuclear SecuritySpecial Nuclear Materials.
References
International Atomic Energy Agency, Nuclear Security Fundamentals, IAEA Nuclear Security Series No. 1, IAEA, Vienna, Austria, 2006.
G. F. Knoll, Radiation Detection and Measurement, 4th ed., John Wiley & Sons, New York, USA, 2010.
S. Agostinelli et al., “Geant4 - a simulation toolkit,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 506, no. 3, pp. 250-303, 2003.
S. Guzii, V. Lukianova, O. Pugach and D. Tuyskyiet, “Removal of residual concentrations of cesium ions from low-level radioactive solutions,” Probl. At. Sci. Technol., no. 4(152), pp. 84-93, 2024, doi: 10.46813/2024-152-084.
D. Charnyi et al., “Adaptation of conventional water treatment technologies for organic component removal from liquid radioactive waste: sorption and coagulation mechanisms,” Sci. Rep., no. 16, 2026, doi: 10.1038/s41598-026-36799-2.
V. Glyva et al., “Design of liquid composite materials for shielding electromagnetic fields,” East.-Eur. J. Enterp. Technol., no. 3(111), pp. 25-31, 2021, doi:10.15587/1729-4061.2021.231479.
S. Guzii, O. Prysiazhna, S. Lapovska, O. Khodakovskyy and V. Pokaliuk, “Influence of porous manganese-containing fillers on the electrodynamic characteristics of absorbing polymer composite materials,” Solid State Phenom., vol. 352(1), pp. 47-55, 2023, doi: 10.4028/p-6D8jIF.
D. K. Fagan, S. M. Robinson and R. C. Runkle, “Statistical methods applied to gamma-ray spectroscopy algorithms in nuclear security missions,” Applied Radiation and Isotopes, vol. 70, no. 10, pp. 2428-2439, 2012, doi:10.1016/j.apradiso.2012.06.016.
J. Allison et al., “Recent developments in Geant4,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 835, pp. 186-225, 2016, doi: 10.1016/j.nima.2016.06.125.
H. Duc Tam, N. T. Hai Yen, L. B. Tran, H. Dinh Chuong and T. Thien Thanh, “Optimization of the Monte Carlo simulation model of NaI(Tl) detector by Geant4 code,” Appl. Radiat. Isot., vol. 130, pp. 75-79, 2017, doi: 10.1016/j.apradiso.2017.09.020.
M. J. Berger et al., XCOM: Photon Cross Section Database (version 1.5), National Institute of Standards and Technology, Gaithersburg, MD, 2010, [Online]. Available: http://physics.nist.gov/xcom.
J. H. Hubbell, “Review of photon interaction cross section data in the medical and biological context,” Phys. Med. Biol., vol. 44, no. 1, pp. R1-R22, 1999.
S. Barbhuiya, B. B. Das and P. Norman, “A comprehensive review of radiation shielding concrete: properties, design, evaluation, and applications,” Struct. Concr., vol. 26, no. 2, pp. 1809-1855, 2025, doi:10.1002/suco.202400519.
R. Singh et al., “Synthetica: Large scale synthetic data for robot perception,” arXiv preprint arXiv:2410.21153, 2024, doi:10.48550/arXiv.2410.21153.
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 23-30, 2017, doi:10.1109/IROS.2017.8202133.