用图神经网络分析小行星危险性，准确率达99%且可解释。
Explainable Deep-Learning Based Potentially Hazardous Asteroids Classification Using Graph Neural Networks
- 将小行星建模为节点，基于轨道和物理特征构建关系图
- 在仅0.22%正样本下，危险小行星召回率达78%
- 可解释性强，适合行星防御与深空导航任务
识别潜在危险小行星（PHAs）对行星防御和深空导航至关重要，但传统方法常忽略小行星间的动力学关联。本文提出一种图神经网络（GNN）方法，将小行星视为节点，利用轨道与物理特征作为节点属性，通过相似性构建边，基于包含958,524条记录的NASA数据集进行训练。尽管存在极端类别不平衡（仅有0.22%为危险标签），模型仍实现整体准确率99%、AUC 0.99，经合成少数类过采样技术处理后，危险小行星的召回率为78%，F1得分为37%。特征重要性分析表明反照率、近日点距离和半长轴为主要预测因子。该框架可支持行星防御任务，并验证了AI在自主导航中的潜力，适用于NASA的近地天体巡天望远镜（NEO Surveyor）和欧空局的Ramses任务，提供可解释且可扩展的小行星危险评估方案。
原文摘要 · Abstract (English)
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 the 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 the Synthetic Minority Oversampling Technique. Feature importance analysis highlights albedo, perihelion distance, and semi-major axis as main predictors. This framework supports planetary defense missions and confirms 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.
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