Browsing by Subject "DAWN"

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  • Martikainen, Julia; Penttilä, Antti; Gritsevich, M.; Videen, Gorden; Muinonen, Karri Olavi (2019)
    We present a new physics-based approach to model the absolute reflectance spectra of asteroid (4) Vesta. The spectral models are derived by utilizing a ray-optics code that simulates light scattering by particles large compared to the wavelength of the incident light. In the light of the spectral data obtained by the Dawn spacecraft, we use howardite powder to model Vesta's surface regolith and its particle size distribution for 10-200 mu m sized particles. Our results show that the modelled spectrum mimics well the observations. The best match was found using a power-law particle size distribution with an index 3.2. This suggests that Vesta's regolith is dominated by howardite particles
  • Siltala, Lauri; Granvik, Mikael (2017)
    Estimates for asteroid masses are based on their gravitational perturbations on the orbits of other objects such as Mars, spacecraft, or other asteroids and/or their satellites. In the case of asteroid-asteroid perturbations, this leads to an inverse problem in at least 13 dimensions where the aim is to derive the mass of the perturbing asteroid(s) and six orbital elements for both the perturbing asteroid(s) and the test asteroid(s) based on astrometric observations. We have developed and implemented three different mass estimation algorithms utilizing asteroid-asteroid perturbations: the very rough 'marching' approximation, in which the asteroids' orbital elements are not fitted, thereby reducing the problem to a one-dimensional estimation of the mass, an implementation of the Nelder-Mead simplex method, and most significantly, a Markov-chain Monte Carlo (MCMC) approach. We describe each of these algorithms with particular focus on the MCMC algorithm, and present example results using both synthetic and real data. Our results agree with the published mass estimates, but suggest that the published uncertainties may be misleading as a consequence of using linearized mass-estimation methods. Finally, we discuss remaining challenges with the algorithms as well as future plans. (C) 2017 Elsevier Inc. All rights reserved.