Probabilistic satellite collision risk prediction via Monte Carlo ensembles

Abstract

The rapid growth of resident space objects in low Earth orbit has significantly increased the risk of satellite collisions, making reliable conjunction risk forecasting a critical operational requirement. Conjunction Data Messages (CDMs), provided by the United States Space Force 18th Space Control Squadron, constitute the primary data source for collision risk assessment. However, modelling CDM data remains challenging due to severe class imbalance, a large concentration of probabilities at the numerical detection floor, and the need for both calibrated probabilistic estimates and accurate risk classification. This paper proposes a hybrid artificial intelligence framework combining Monte Carlo probabilistic modelling with ensemble machine learning. The proposed approach integrates gradient boosting models and deep neural networks within a stacked ensemble architecture, while event-level sequence models are additionally evaluated for their ability to capture temporal patterns across conjunction trajectories. A calibration strategy is introduced to improve the reliability of predicted collision probabilities. Experiments conducted on the ESA Spacecraft Collision Avoidance Challenge dataset, comprising 199,082 Conjunction Data Messages across 15,321 conjunction events, demonstrate strong predictive performance. The proposed framework achieves an F2 score of 0.808 and an AUC-ROC of 0.981 while maintaining well-calibrated probabilistic forecasts. These results demonstrate the potential of hybrid probabilistic–ensemble approaches for improving AI-driven satellite collision risk assessment.