Optimization of the LaBr₃ detector model using differential evolution algorithms.

Area of Science:

• Nuclear Physics
• Analytical Chemistry
• Computational Physics

Background:

• Accurate detector efficiency is vital in gamma-ray spectrometry for quantifying radioactive materials.
• Computational detector models offer a method to determine efficiency for non-standard sources without physical calibration standards.
• Lanthanum bromide (LaBr3(Ce)) detectors are widely used due to their excellent timing and energy resolution.

Purpose of the Study:

• To develop and optimize a computational model for a 1.5″ × 1.5″ LaBr3(Ce) scintillation detector.
• To validate the model's accuracy in predicting detector efficiency using experimental data.
• To assess the model's applicability for non-standard sources in gamma-ray spectrometry.

Main Methods:

• Detector characterization using 137Cs and 152Eu point sources at various positions.
• Monte Carlo simulation using MCNP6 code for computational modeling.
• Model optimization employing differential evolution algorithms to reconcile calculated and experimental responses.

Main Results:

• Initial MCNP6 model showed deviations between simulated and experimental detector responses.
• Optimization using differential evolution algorithms successfully refined detector parameters.
• The optimized LaBr3(Ce) model demonstrated accurate predictions when verified with a KCl volumetric source, with deviations within error limits.

Conclusions:

• An optimized computational model for a LaBr3(Ce) detector was successfully developed.
• The validated model accurately determines detector efficiency, particularly for non-standard sources.
• This approach enhances the capabilities of gamma-ray spectrometry by reducing reliance on physical calibration standards.