A data scientist has developed a supervised machine learning pipeline to identify potential satellite collisions. The system uses Random Forest and XGBoost models trained on synthetic data to flag close approaches with a collision probability exceeding a 10⁻⁴ threshold. Key findings indicate that raw input features performed as well as engineered ones, and while the XGBoost model achieved a high ROC-AUC score, its effectiveness in predicting actual high-risk scenarios was limited even after calibration. AI
IMPACT This project demonstrates a practical application of ML for improving efficiency in satellite collision avoidance, potentially reducing manual workload for flight dynamics analysts.
RANK_REASON The item describes a personal project applying machine learning models to a specific problem, rather than a formal research paper or a product release. [lever_c_demoted from research: ic=1 ai=1.0]
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