Physics-based optimization of gamma-ray detector response and pair production cross-sections using the GRIBO...

Area of Science:

• Nuclear Physics
• Computational Physics

Background:

• Nonlinear and multivariate optimization are challenging in radiation physics and nuclear instrumentation.
• Existing optimization algorithms may lack accuracy and physical interpretability for these complex problems.

Purpose of the Study:

• To present an enhanced Gamma Ray Interactions Based Optimization (GRIBO) algorithm.
• To apply the improved GRIBO to problems in gamma spectrometry, including detector parameter extraction and cross-section parameterization.

Main Methods:

• Developed a physics-informed metaheuristic algorithm embedding gamma-ray interaction processes (photoelectric absorption, Compton scattering, pair production).
• Applied the enhanced GRIBO to Gaussian energy broadening in NaI(Tl) detectors and pair production cross-sections in silicon.

Main Results:

• Accurately reproduced experimental FWHM values for NaI(Tl) detectors.
• Outperformed genetic algorithm, PSO, GSA, and GWO in convergence speed and stability.
• Reduced maximum relative error for silicon pair production cross-sections to 1.24%, below competing algorithms (>2%).

Conclusions:

• The enhanced GRIBO demonstrates accuracy and physical interpretability.
• The physics-based approach offers robustness in noisy or data-sparse conditions.
• Provides a versatile framework for detector modeling and nuclear data analysis in nuclear science.