Near-earth asteroids classification using NGBoost classifier

Abstract

Near-Earth asteroids (NEAs) are celestial bodies that orbit within close to Earth, offering valuable insights into the early solar system's formation and posing potential hazards due to impact events. This work presents a comprehensive overview of NEAs, encompassing their historical significance, characteristics, impact hazards, and prospects. The study outlines the NASA Asteroids Classification Dataset and discusses its importance for research on asteroid classification and risk assessment. Furthermore, the methodology section delineates the utilization of the NGBoost classifier for predictive modeling tasks, detailing data collection, preprocessing, model training, evaluation, and result interpretation. Results from the NGBoost classifier demonstrate high accuracy and performance metrics in classifying asteroids, underscoring its efficacy in advancing asteroid classification efforts and informing planetary defense strategies. NEAs pose a potential threat to our planet, and their classification is essential for understanding their properties and predicting their trajectories accurately. In this research, we explore the application of NGBoost, a powerful gradient-boosting framework, for classifying NEAs based on their orbital and physical characteristics. We present a dataset comprising features extracted from known NEAs and non-NEAs and demonstrate the efficacy of NGBoost in accurately distinguishing between these classes. Our results indicate promising performance metrics with 99.22% accuracy, suggesting that NGBoost holds potential as a valuable tool in asteroid classification.