For this challenge, we invite you to become "virtual contributors" to the Asteroid Grand Challenge and develop a hypothetical method, concept note or simple prototype that demonstrates how Machine Learning could be used to help us avoid the same fate as the dinosaurs.
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.
Sample Areas to Explore:
Tip: The Minor Planet Center currently acts as the central clearinghouse for asteroid observations taken as data from professional and amateur telescopes, and space-based observatories such as NASA’s NEOWISE. This astrometric data allows the calculation of orbits for the asteroids so both professionals and amateurs may conduct follow-up observations from the ground.