Lucifer's Screwdriver received a Global Nomination.
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.
Lucifer's Screwdriver is a project created for the NASA Space Apps 2016 challenge: "Near Earth Objects Machine Learning".
This is a proof-of-concept for using machine learning to identify Potentially Hazardous Asteroids (PHA). The standard definition of a PHA is an asteroid whose closest approach to Earth is below a threshold, and whose brightness is over a threshold. We use an existing tool to compute the distance of closest approach from orbital parameters reported by the Minor Planet Center, and then train a supervised classifier to identify the PHAs.
Coding: Daniel Wysocki
Video: Juan Lachapelle, Justin Flory, and Daniel Wysocki
Logo: Guan-Huei Wu
Minor Planet Center database, Python, Numpy, Scikit Learn