Explainable Deep-Learning Based Potentially Hazardous Asteroids Classification Using Graph Neural Networks

--Classifying potentially hazardous asteroids (PHAs) is crucial for planetary defense and deep space navigation, yet traditional methods often overlook the dynamical relationships among asteroids. We introduce a Graph Neural Network (GNN) approach that models asteroids as nodes with orbital and physical features, connected by edges representing their similarities, using a NASA dataset of 958,524 records. Despite an extreme class imbalance with only 0.22% of the dataset with hazardous label, our model achieves an overall accuracy of 99% and an AUC of 0.99, with a recall of 78% and an F1-score of 37% for hazardous asteroids after applying Synthetic Minority Oversampling T echnique. Feature importance analysis highlights albedo, perihelion distance, and semi-major axis as main predictors. This framework supports planetary defense missions and confirm AI's potential in enabling autonomous navigation for future missions such as NASA's NEO Surveyor and ESA's Ramses, offering an interpretable and scalable solution for asteroid hazard assessment. However, a small subset known as potentially hazardous asteroids (PHAs) follow orbits that bring them perilously close to our planet, raising the specter of catastrophic collisions. Historical events, such as the 1908 Tunguska explosion [1], which devastated over 2,000 square kilometers of Siberian forest, and the 2013 Chelyabinsk meteor [2], which injured over 1,000 people and caused widespread property damage, show the destructive potential of these celestial bodies.