DEPARTMENT OF NUCLEAR PHYSICS - NUCLEAR ENGINEERING - MEDICAL PHYSICS

A dual-energy gamma-ray transmission method using ¹³⁷Cs and ²⁴¹Am sources for determining effective atomic number and mass attenuation coefficient of lightweight materials

Le Thi Ngoc Trang, Huynh Dinh Chuong, Vo Hoang Nguyen, Tran Thien Thanh

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 1086 (2026) 171390

Abstract

This paper presents a dual-energy gamma-ray transmission method for determining the effective atomic number (Z_eff) and mass attenuation coefficient (MAC) of lightweight materials. The experimental setup consists of sealed ¹³⁷Cs and ²⁴¹Am sources and a NaI(Tl) detector arranged in a narrow-beam transmission geometry to measure gamma rays transmitted through the sample. A corresponding MCNP6 model was developed to closely reproduce the experimental configuration. Using this model, pulse-height spectra were simulated for a set of materials with atomic numbers from 1 to 20, thicknesses between 1 and 4 cm, and densities from 0.6 to 3.0 g cm⁻³. The Z_eff of a material was determined by exploiting the ratio of logarithmic attenuations measured at 59.5 keV and 661.7 keV, using a calibration curve established from the simulation data, without requiring any prior knowledge of the sample thickness, density, and elemental composition. In addition, an analytical model was constructed to describe the dependence of the MAC on both atomic number and photon energy, for atomic numbers from 1 to 20 and photon energies in the range 50 keV to 20 MeV. This model enables the MAC of a material to be estimated at various photon energies directly from its previously determined Z_eff. The proposed method was validated for several lightweight materials, including graphite, aluminum, polymers, pine wood, brick, glass, concrete, and stone, using both simulated and experimental data. The results show that the Z_eff values obtained by this method are well correlated with the elemental composition of the materials. For materials that either do not contain hydrogen or contain it only in low concentrations, the MAC values estimated by the proposed method exhibit generally good agreement with reference XCOM data, whereas for materials with a high hydrogen content the discrepancies of up to approximately 16.8% are observed.

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A comparative study of machine learning and conventional methods for determining the dead layer thickness of an HPGe detector

N.D. Thong, N.H.K. Vi, L.N.D. Uyen, N.V. Thiem, T.T. Thanh, V.T. Minh, P.L. Ho, C.T. Tai, C.V. Tao

Radiation Physics and Chemistry 249(2026)114162

Abstract:

The dead layer of p-type HPGe detectors grows progressively due to lithium diffusion, degrading detection efficiency at low gamma-ray energies. This study compares four regression approaches for dead layer estimation— conventional G4-scan interpolation, single-source Linear Regression (LR), single-source Random Forest (RF), multi-source LR and multi-source RF combining ²⁴¹Am and ¹⁰⁹Cd — applied to an ORTEC GEM50P4-83 detector at two epochs separated by eight years. A single Geant4 simulation campaign (N = 10⁷ events/run, 1301 points) trained all models, with GUM-compliant uncertainties throughout. All four ML predictions agree with the G4-scan reference (1.323 ± 0.019 mm) within 0.006 mm for the 2018 dataset. When Beer–Lambert linearity is confirmed (R² > 0.998) and features are restricted to 𝑙𝑛(𝜖), Linear Regression achieves a crossvalidated MAE of 0.009 mm, outperforming all tree-based benchmarks (MAE = 0.010 mm), consistent with the Gauss–Markov theorem. A quantitative threshold analysis shows that multi-source LR reduces total uncertainty by 27% when 𝛿𝜖Cd ∕𝜖Cd < 2.04% — a condition satisfied by the present measurements. Dead layer growth rates of 0.208 mm/year (2015–2018) and 0.009 mm/year (2018–2026) are consistent with nonlinear lithium diffusion deceleration.

More detail: https://doi.org/10.1016/j.radphyschem.2026.114162

A benchmark for Monte Carlo simulations in gamma-ray spectrometry Part II: True coincidence summing correction factors

M.-C. Lépy, C. Thiam, M. Anagnostakis, C. Cosar, A. de Blas, H. Dikmen, M.A. Duch, R. Galea, M.L. Ganea, S. Hurtado, K. Karfopoulos, A. Luca, G. Lutter, I. Mitsios, H. Persson, C. Potiriadis, S. Röttger, N. Salpadimos, M.I. Savva, O. Sima, T.T. Thanh, R.W. Townson, A. Vargas, T. Vasilopoulou, L. Verheyen, T. Vidmar

 Applied Radiation and Isotopes, 2023

Abstract:

The goal of this study is to provide a benchmark for the use of Monte Carlo simulation when applied to coincidence summing corrections. The examples are based on simple geometries: two types of germanium detectors and four kinds of sources, to mimic eight typical measurement conditions. The coincidence corrective factors are computed for four radionuclides. The exercise input files and calculation results with practical recommendations are made available for new users on a dedicated webpage.

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A benchmark for Monte Carlo simulation in gamma-ray spectrometry

M.C. Lépy, C. Thiam, M. Anagnostakis, R. Galea, D. Gurau, S. Hurtado, K. Karfopoulos, J. Liang, H. Liu, A. Luca, I. Mitsios, C. Potiriadis, M.I. Savva, T.T. Thanh, V. Thomas, R.W. Townson, T. Vasilopoulou, M. Zhang

Abstract: