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| Titel |
Shapes, rotation, and pole solutions of the selected Hilda and Trojan asteroids |
| VerfasserIn |
Maria Gritsevich, Sarah Sonnett, Johanna Torppa, Amy Mainzer, Karri Muinonen, Antti Penttilä, Thomas Grav, Joseph Masiero, James Bauer, Emily Kramer |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250151751
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| Publikation (Nr.) |
 EGU/EGU2017-16507.pdf |
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| Zusammenfassung |
| Binary asteroid systems contain key information about the dynamical and chemical environments in which they formed. For example, determining the formation environments of Trojan and Hilda asteroids (in 1:1 and 3:2 mean-motion resonance with Jupiter, respectively) will provide critical constraints on how small bodies and the planets that drive their migration must have moved throughout Solar System history, see e.g. [1-3]. Therefore, identifying and characterizing binary asteroids within the Trojan and Hilda populations could offer a powerful means of discerning between Solar System evolution models. Dozens of possibly close or contact binary Trojans and Hildas were identified within the data obtained by NEOWISE [4]. Densely sampled light curves of these candidate binaries have been obtained in order to resolve rotational light curve features that are indicative of binarity (e.g., [5-7]).
We present analysis of the shapes, rotation, and pole solutions of some of the follow-up targets observed with optical ground-based telescopes.
For modelling the asteroid photometric properties, we use parameters describing the shape, surface light scattering properties and spin state of the asteroid. Scattering properties of the asteroid surface are modeled using a two parameter H-G12 magnitude system. Determination of the initial best-fit parameters is carried out by first using a triaxial ellipsoid shape model, and scanning over the period values and spin axis orientations, while fitting the other parameters, after which all parameters were fitted, taking the initial values for spin properties from the spin scanning. In addition to the best-fit parameters, we also provide the distribution of the possible solution, which should cover the inaccuracies of the solution, caused by the observing errors and model. The distribution of solutions is generated by Markov-Chain Monte Carlo sampling the spin and shape model parameters, using both an ellipsoid shape model and a convex model, Gaussian curvature of which is defined as a spherical harmonics series [8].
References: [1] Marzari F. and Scholl H. (1998), A&A, 339, 278. [2] Morbidelli A. et al. (2005), Nature, 435, 462. [3] Nesvorny D. et al. (2013), ApJ, 768, 45. [4] Sonnett S. et al. (2015), ApJ, 799, 191. [5] Behrend R. et al. (2006), A&A, 446, 1177. [6] Lacerda P. and Jewitt D. C. (2007), AJ, 133, 1393. [7] Oey J. (2016), MPB, 43, 45. [8] Muinonen et al., ACM 2017. |
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