The primary aim of this study is to classify space radiation using existing models trained and analysed with a prepared dataset. Leveraging techniques such as Logistic Regression, Decision Tree, Random Forest, and Ensemble methods, the research aims to classify various types of space radiation, including gamma rays and hadrons. The goal is to build a classification prediction system capable of accurately distinguishing between different types of space radiation, facilitating the effective identification and analysis of radiation-induced effects in semiconductor devices. By discriminating between various types of radiation, this study aids in the detection and characterization of radiation-induced effects, crucial for evaluating the reliability and performance of semiconductor devices in space conditions. The outcomes of this research contribute to advancing the understanding of space radiation effects on semiconductor devices and assist in devising mitigation strategies to enhance their resilience in space missions. The study found that Random Forest and XGBoost were the top performers, achieving 99% accuracy in classifying space radiation, and Decision Tree also showed strong results at 98% accuracy.

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