Optimization of Variance Reduction Techniques used in EGSnrc Monte Carlo Codes

Area of Science:

• Medical Physics
• Computational Physics
• Radiation Dosimetry

Background:

• Monte Carlo (MC) simulations are crucial for accurate radiation transport calculations but are computationally intensive.
• Significant simulation time is spent on non-important events, necessitating variance reduction techniques (VRTs).
• The Electron Gamma Shower (EGSnrc) code offers a comprehensive suite of VRTs, including photon splitting, Russian roulette (RR), and photon cross-section enhancement (XCSE).

Purpose of the Study:

• To identify the optimal combination of VRTs for enhancing MC simulation speed and efficiency.
• To evaluate VRTs without compromising the accuracy of radiation dose calculations.
• To determine the most effective VRTs for ion chamber dosimetry using EGSnrc.

Main Methods:

• Utilized EGSnrc MC User codes (cavity, egs_chamber) to simulate ion chamber geometries.
• Employed 6 MV photon beams and 1.25 MeV ⁶⁰Co photon beams for simulations.
• Systematically evaluated combinations of VRTs, focusing on XCSE and RR.

Main Results:

• The combination of XCSE and RR demonstrated the highest efficiency for ion chamber dose calculations.
• Optimal VRT selection was achieved within a 30 cm × 30 cm × 30 cm water phantom.
• The study confirmed that specific VRT combinations can increase simulation efficiency without altering fundamental physics.

Conclusions:

• The optimal VRT combination of XCSE and RR significantly enhances MC simulation efficiency for ion chamber dose calculations.
• Proper VRT selection is key to improving computational speed and resource utilization in radiation dosimetry.
• EGSnrc's VRTs provide valuable tools for optimizing MC simulations in radiation transport and dosimetry applications.