Machine Learning in Planetary Defense Early Warning Systems for Hazardous Asteroids

In the ever-evolving realm of planetary defense, the pivotal role of Machine Learning has come to the forefront, serving as an indispensable tool for early warning systems dedicated to predicting the orbits and trajectories of hazardous asteroids. By harnessing a versatile array of advanced algorithms, including decision trees, k-nearest neighbors (KNN), XGBoost, logistic regression, and multi-layer perceptron (MLP), a community of dedicated researchers is diligently crafting robust models that hold the key to categorizing asteroids into distinct orbit classes. These classes, notably the Near-Earth Objects (NEOs) and potentially hazardous asteroids, furnish us with invaluable insights to assess risk levels and formulate effective mitigation strategies. The fusion of artificial intelligence and astronomy has, thus, facilitated a profound leap in our capacity to scrutinize the data and characteristics of these celestial bodies, shedding light on their enigmatic movements and the potential threats they may pose. Through this novel approach, we are empowered to cultivate a deeper understanding of the dynamic cosmos and, concurrently, to enhance our ability to safeguard Earth against the looming specter of potential asteroid impacts. In an age where space exploration and the mysteries of the universe beckon with both wonder and peril, this synergy of technology and science stands as an emblem of human ingenuity and determination to protect our home planet.

 Keywords: Decision Tree, KNN, XBoost, Logistic Regression, MLP.