A Data-Driven Variance Reduction Technique for Efficiently Modelling Astronaut Radiation Doses in Spacecraft in...

Area of Science:

• Space science
• Radiation physics
• Computational physics

Background:

• Space missions expose astronauts to ionizing radiation, increasing health risks like cancer.
• Estimating radiation exposure relies on computationally intensive particle transport simulations.
• Current simulation methods require numerous particle histories for low statistical uncertainty.

Purpose of the Study:

• To develop and validate a novel variance reduction technique for space radiation simulations.
• To reduce computational time in Monte Carlo simulations for astronaut radiation exposure.
• To enable efficient assessment of shielding materials and configurations for spacecraft.

Main Methods:

• Developed a custom application using the Geant4 Toolkit for 3D particle trajectory and energy computation.
• Applied a directional source bias to an isotropic radiation field (e.g., galactic cosmic rays).
• Utilized the Monte Carlo method to simulate particle interactions within the International Space Station environment.

Main Results:

• The variance reduction technique significantly reduces simulation time while maintaining statistical uncertainty.
• Demonstrated the impact of the technique on effective dose equivalence based on particle type, geometry, and materials.
• The method allows for tunable optimization of simulation speed based on user objectives.

Conclusions:

• The directional source bias variance reduction technique is effective for space radiation simulations.
• This method facilitates rapid testing of spacecraft shielding and improves computational efficiency.
• The technique is easily implemented with provided recommended parameters for practical application.