The early DNA damage on the surface of the Moon due to GCR protons and alpha particles were assessed using a multiscale approach in Geant4. This consisted of three simulation stages. A periodic boundary conditions approach was used to obtain the radiation field on the surface and inside a proposed lunar habitat. The radiation field on the cellular scale was obtained in the organs of male and female astronauts using the ICRP145 tetrahedral mesh phantoms. This was subsequently simulated using a full human cell model in Geant4-DNA to obtain the early DNA damage. Geant4-DNA track structure ionisation models upper energy limits were extended to be able to model the interactions of the GCR at sub-cellular level, covering an energy range from a few eV up to 1 TeV. Hadronic interactions and the modelling of induced radiochemical species were also implemented. The early DNA damage was assessed using the Geant4-DNA molecularDNA example. A greater yield of DNA damage was observed on the lunar surface compared with the habitat, and indirect damage due to induced hydroxyl radicals constituted most of the damage. This study demonstrates a complete simulation pipeline for the assessment of early DNA damage in astronauts in the space radiation environment.

Low-cost muon detectors utilising cheap plastic scintillators and a limited number of individual silicon photomultipliers (SiPMs) offer a compelling approach to cheap experimental designs, provided the event localisation of a traversing particle can be accurately determined. In this theoretical work, we use Geant4 to simulate a diverse range of detector configurations, shapes and SiPM photosensors, predicting the light intensity received at a given SiPM. Testing a range of methods to localise muon events we determine that machine learning techniques outperform analytic models, and of these, a simple gradient boosted framework is the most reliably accurate localisation technique for our simulated scintillators. We find that a simple square scintillator outperforms other geometries and that AI performs, when applied to this shape, with a linear relationship between the positional accuracy of the event recovery and the average distance between photosensors around the detector perimeter.