Monte Carlo simulation of coherently scattered photons based on the inverse‐sampling technique. Issue 1 (5th December 2019) - Muhammad, Wazir; Liang, Ying; Hart, Gregory R.; Nartowt, Bradley J.; Deng, Jun
Abstract : A novel Rayleigh scattering model using anomalous scattering factors based on the inverse‐sampling technique was implemented in a general‐purpose Monte Carlo radiation transport code. The updated Monte Carlo model provides a more accurate representation of Rayleigh scattering with improved computational efficiency. Abstract : The acceptance–rejection technique has been widely used in several Monte Carlo simulation packages for Rayleigh scattering of photons. However, the models implemented in these packages might fail to reproduce the corresponding experimental and theoretical results. The discrepancy is attributed to the fact that all current simulations implement an elastic scattering model for the angular distribution of photons without considering anomalous scattering effects. In this study, a novel Rayleigh scattering model using anomalous scattering factors based on the inverse‐sampling technique is presented. Its performance was evaluated against other simulation algorithms in terms of simulation accuracy and computational efficiency. The computational efficiency was tested with a general‐purpose Monte Carlo package named Particle Transport in Media ( PTM ). The evaluation showed that a Monte Carlo model using both atomic form factors and anomalous scattering factors for the angular distribution of photons (instead of the atomic form factors alone) produced Rayleigh scattering results in closer agreement with experimental data. The comparison and evaluation confirmed that the inverse‐sampling technique using atomic form factors and anomalous scattering factors exhibited improved computational efficiency and performed the best in reproducing experimental measurements and related scattering matrix calculations. Furthermore, using this model to sample coherent scattering can provide scientific insight for complex systems.