Asteroid orbits with Gaia using random-walk statistical ranging. (April 2016)

• Record Type: Journal Article
• Authors: Muinonen, Karri; Fedorets, Grigori; Pentikäinen, Hanna; Pieniluoma, Tuomo; Oszkiewicz, Dagmara; Granvik, Mikael; Virtanen, Jenni; Tanga, Paolo; Mignard, François; Berthier, Jérôme; Dell׳Oro, Aldo; Carry, Benoit; Thuillot, William
• Is Part Of: Planetary and space science. Volume 123(2016)
• Page Start: 95
• Page End: 100
• Publication Date: 2016-04
• Subjects: Asteroids -- Near-Earth objects -- Orbital inversion -- Markov-chain Monte Carlo methods -- Random-walk Metropolis–Hastings algorithm -- Gaia mission; Space sciences -- Periodicals; Atmosphere, Upper -- Periodicals; Sciences spatiales -- Périodiques; Haute atmosphère -- Périodiques; 523
• DOI: 10.1016/j.pss.2015.10.010
• Languages: English
• ISSNs: 0032-0633

Abstract

Abstract: We describe statistical inverse methods for the computation of initial asteroid orbits within the data processing and analysis pipeline of the ESA Gaia space mission. Given small numbers of astrometric observations across short time intervals, we put forward a random-walk ranging method, in which the orbital-element phase space is uniformly sampled, up to a limiting χ 2 -value, with the help of the Markov-chain Monte Carlo technique (MCMC). The sample orbits obtain weights from the a posteriori probability density value and the MCMC rejection rate. For the first time, we apply the method to Gaia astrometry of asteroids. The results are nominal in that the method provides realistic estimates for the orbital uncertainties and meets the efficiency requirements for the daily, short-term processing of unknown objects. Highlights: Random-walk ranging method is offered for asteroid initial orbital inversion. First asteroid orbits are computed from Gaia astrometry using the method. Random-walk ranging proves to be accurate, robust, and efficient. The method is implemented in the Gaia data-processing and analysis pipeline.