Cutting-Edge Strategies for Radiation Effect Estimation on Asteroids Space Mission

Assessing the reliability of electronic devices in space is crucial, yet current methodologies often lack realism in reflecting actual mission scenarios. State-of-the-art approaches for Total Ionizing Dose (TID) and Single Event Effects (SEE) estimation face limitations in accurately capturing trajectory-dependent radiation exposure and technology-specific device sensitivity. To address these challenges, we propose advanced methodologies that enhance radiation risk assessment through machine learning and empirical data integration. Our ML-based TID estimation tool predicts radiation dose based on interplanetary trajectory, mission duration, and shielding thickness. Polynomial regression models, trained on a SPENVIS-generated dataset, enable precise dose estimation by discretizing trajectories and selecting the appropriate model for each segment. Additionally, SEE sensitivity is evaluated using cross-section data from publicly available radiation test databases, combined with particle flux estimates from radiation environment toolS, ensuring technology-aware predictions. Applied to the Zodiac Pioneer mission feasibility study, our approach provides a comprehensive and efficient radiation environment analysis, surpassing traditional methods in both accuracy and automation.