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.
Using Genetics algorithm with the orbital parameters as the equivalent of chromosomes, the complete set of which compose an individual: the orbit. For each individual, we can compute a function of performance which will be used to compare it to others.
Using the orbit inversion code named Genoid (GENetic Orbit IDentification), relies on a metaheuristic method and a dynamical model of the solar system. Using two dynamical models: a simple Keplerian model and a full N-body problem which includes the gravitational field of the primary asteroid up to 4th order.
To minimize the search-time for an orbital solution, we first used the
Keplerian model to explore a wide space of solutions, even based on a
limited number of astrometric positions. We then ran the full
N-body
model on a set of parameters around the Keplerian solution to search
for the most accurate solution when taking into account the full
physical behavior of dynamical systems.
The implementation will run in python or java program that will develop the model, to chaise Hazardous Asteroid (PHA), Near Earth Asteroid (NEA), or Known Astroid (KA) . using genetic algorithms with our own secreat sauce as metaheuristic.
https://spaceapps.hackpad.com/FIEC-MACHINE-LEARGNI...
methahuristic genetics algorithms,, numpy, scypy, DEAP Python, Genoid Algorithm, mino planet center data