Temporal Trends in Asteroid Behavior: A Machine Learning and N-Body Integration Approach

Abstract

Asteroids pose significant threats to Earth, necessitating early detection for potential deflection. Leveraging machine learning (ML), we classify asteroids into Near-Earth Asteroids (particularly Atens, Amors, Apollos, and Apoheles) and Non Near-Earth Asteroids, further categorizing them based on hazard potential. Training the seven models on a comprehensive dataset of 4687 asteroids, we achieve high accuracy in prediction. The predictive capability of these models is critical for informed decision-making in planetary defense strategies. We apply different regularization techniques to prevent overfitting and validate the models using a large unseen dataset. A rigorous long-term N-body integration spanning 1 million years is executed utilizing the Mercury N-body integrator to illuminate the evolution of asteroid properties over extended temporal scales. Following this integration process, the best-performing ML model is employed to classify asteroids based on their orbital characteristics and hazardous status respectively. Our findings highlight the effectiveness of ML in asteroid classification and prediction, paving the way for large-scale applications. By dividing a 1 million-year integration into intervals, we uncover temporal trends in asteroid behavior, revealing insights into hazard evolution and ejection patterns. Notably, initially, hazardous asteroids tend to transition to non-hazardous states over time, elucidating key dynamics in planetary defense. We illustrate these findings through plotted graphs, providing valuable insights into asteroid dynamics. These insights are instrumental in advancing our understanding of long-term asteroid behavior, with significant implications for future research and planetary protection efforts.