Calculation of absorbed dose ratios using correlated Monte Carlo sampling

Area of Science:

• Computational physics
• Medical physics
• Radiation dosimetry

Background:

• Monte Carlo methods are crucial for simulating radiation transport.
• Calculating correction factors for radiation dosimeters requires accurate absorbed dose determination.
• Variance reduction techniques are essential for improving the efficiency of Monte Carlo simulations.

Purpose of the Study:

• To implement and evaluate a correlated sampling variance reduction technique in the EGS4 Monte Carlo code.
• To calculate correction factors for radiation dosimeters using this enhanced method.
• To assess the efficiency and accuracy gains of correlated sampling for absorbed dose ratio calculations.

Main Methods:

• Implementation of correlated sampling within the EGS4 (Electron Gamma Shower version 4) Monte Carlo code.
• Calculation of absorbed dose ratios in various geometries (thin slabs, ionization chambers) under photon and electron irradiation.
• Theoretical analysis of the relationship between correlation degree and calculation efficiency/accuracy.

Main Results:

• Correlated sampling significantly increases calculation efficiency, achieving up to a thousand-fold gain for specific ionization chamber parameters.
• Improved accuracy (reduced systematic uncertainty) is observed when good correlation exists between calculated doses in similar geometries.
• The efficiency and accuracy gains are directly related to the degree of correlation between simulated doses.

Conclusions:

• Correlated sampling is a highly effective variance reduction technique for Monte Carlo dosimetry calculations.
• This method offers substantial improvements in computational efficiency and accuracy for determining radiation dosimeter correction factors.
• The findings support the broader application of correlated sampling in complex radiation transport simulations.