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

The ionizing radiation exposure to crew on current and future space missions can significantly increase their health risks for cancers, degenerative diseases, and other acute and late effects. A common approach for estimating risk to crew is by completing stochastic (e.g., Monte Carlo) or deterministic particle transport simulations. Within the simulated environment, a small fraction of the particle histories tracked will interact with the astronaut or detector, particularly for larger spacecraft such as the International Space Station, Tiangong Space Station or Lunar Gateway. These simulations can be computationally intensive as they require a very large number of particle histories to achieve a low statistical uncertainty. Variance reduction techniques are applied to simulations to reduce the computational time of the simulation while maintaining the same (or less) statistical uncertainty. The variance reduction technique developed herein involves applying a directional source bias to an isotropic radiation field, such as galactic cosmic rays, to reduce the quantity of particles that have a low probability of interacting with the astronaut or detector. A custom application has been developed utilizing the Geant4 Toolkit that computes the trajectories and energies of particles in three dimensions in the International Space Station using the Monte Carlo method. The results demonstrate the impact of our variance reduction technique on effective dose equivalence depending on: primary and secondary particle type (proton, neutron, photon, heavy ion, etc.), geometric volumes and spacecraft materials. Our variance reduction technique can be tuned by the user to optimize the simulation time depending on their objectives and enables rapid testing of different shield configurations and materials. This variance reduction technique is implemented easily using several input parameters for boundary conditions. Recommended values are presented for rapid implementation in simulations.