NEAR EARTH OBJECTS MACHINE LEARNING

BACKGROUND

There are millions of yet undiscovered Near Earth Objects (NEOs) which could pose a threat to Planet Earth. These Asteroids require space-based hardware to locate and track, however once their position is identified, follow-up observations can be made with radar or optical telescopes gathering light curve data - enabling estimates of composition, reflectivity, rotation and other characteristics that inform mitigation strategies to deflect objects before they impact with Earth. Presently, only a handful of hazardous NEOs have been detected prior to entering our atmosphere. The immense task of asteroid hunting is further complicated by the high number of false positives and long duration between observations - where some NEOs have orbits of many decades. Presented with these challenges, the space community has begun to look towards

"machine learning" to both mechanize and accelerate the speed of detection and characterization.