Exploring Ionizing Radiation at Aviation Altitudes Through Machine Learning and Muon Detection

Cumulative exposure to ionizing radiation at aviation altitudes poses significant health risks for aircrews and, at higher altitudes, astronauts. Physics-based models are commonly used to estimate radiation levels during flight; however, they often do not fully capture the rapidly varying and complex nature of atmospheric radiation, limiting real-time prediction accuracy.To address this limitation, we explore machine learning (ML) approaches to improve the analysis and nowcasting of aviation radiation.Using newly compiled, ML-ready aviation radiation datasets, we train supervised ML models to identify nonlinear relationships between geospace environmental parameters and measured radiation effective dose rates. Our results show that a gradient boosting (XGBoost) model trained on the concurrent properties of the geospace environment improves radiation prediction accuracy by ~9% compared to the considered physics-based NAIRAS-v3 model. Feature importance analysis and Shapley Additive Explanations (SHAP) indicate key geospace parameters, including solar wind and solar polar fields, play a dominant role in controlling radiation variability at flight altitudes.In a complementary observational study, we examine the role of secondary cosmic-ray muons in aviation radiation environments at altitudes below 15 km. Atmospheric muon flux measurements obtained from a CubeSat prototype developed by the Nuclear Physics Group at Georgia State University are analyzed alongside radiation doses modeled by NAIRAS-v3. Correlation analysis demonstrates a strong, statistically significant positive relationship between measured muon counts per minute and modeled radiation dose rates (µSv/h), with a Pearson correlation coefficient of r = 0.93.