GaussSpectraSwift software for variance-reduced Gaussian energy broadening and resolution function fitting in radiation detector response simulations - Hila, Frederick C.; Astronomo, Alvie A.; Amorsolo, Alberto V., Jr.; Mercado, Candy C. (2025)
This work introduces GaussSpectraSwift, a computational toolkit (publicly available at https:// gammaraysim.coe.upd.edu.ph/gaussspectraswift/) designed to streamline the Gaussian energy broadening (GEB) process in Monte Carlo simulations for gamma-ray detector response modeling. The software allows users to choose between deterministic or stochastic broadening methods. The deterministic method minimizes variance by eliminating Monte Carlo sampling uncertainties, whereas the stochastic approach preserves these uncertainties, enabling evaluation of the raw input spectrum’s quality. Additionally, GaussSpectraSwift provides a robust realtime platform for nonlinear energy-resolution function fitting of experimental data points. The curve-fitting capabilities of GaussSpectraSwift were validated against the SciPy package in Python, and its broadening methodologies were evaluated using raw unbroadened input spectra derived from PHITS simulations. The study reports that the uniform bin size of the raw input spectrum plays a crucial role in the performance of GaussSpectraSwift. By choosing an appropriate bin size, GaussSpectraSwift can facilitate a more accurate and potentially variance-reduced broadening process. Overall, GaussSpectraSwift offers a practical solution for expediting and enhancing the GEB implementation in Monte Carlo simulations for gammaray detector response modeling.
Keywords: Gaussian Processes