Prediction of Potentially Hazardous Asteroids using Deep Learning

An impact by an asteroid is one of the very few natural disasters that could be mitigated or even entirely prevented if accurate predictions are made early enough. Classifying Near-Earth Asteroid population to identify Potentially Hazardous Asteroids well before an impact is one of the most important problems that need to be solved to avoid humanity from facing the same fate as that of the dinosaurs 65 million years ago. This study presents an approach to classify the Near-Earth Asteroid Population as potentially hazardous or non-hazardous by allowing deep neural networks to learn complex representations that exist in the distribution of available asteroid orbital data. We believe that the generation of an automatic potentially hazardous asteroid detector would contribute to speed up the characterization rate of the rapidly growing asteroid data that are made available through advances of science.