Asteroid orbital ranging using Markov-Chain Monte Carlo

Abstract

We present a novel Markov-Chain Monte-Carlo orbital ranging method (MCMC) for poorly observed single-apparition asteroids with two or more observations. We examine the Bayesian a posteriori probability density of the orbital elements using methods that map a volume of orbits in the orbital-element phase space. In particular, we use the MCMC method to sample the phase space in an unbiased way. We study the speed of convergence and also the efficiency of the new method for the initial orbit computation problem. We present the results of the MCMC ranging method applied to three objects from different dynamical groups. We conclude that the method is applicable to initial orbit computation for near-Earth, main-belt, and transneptunian objects.