Asteroids' physical models from combined dense and sparse photometry and scaling of the YORP effect by the observed obliquity distribution

2013
The larger number of models of asteroidshapes and their rotational states derived by the lightcurve inversion give us better insight into both the nature of individual objects and the whole asteroidpopulation. With a larger statistical sample we can study the physical properties of asteroidpopulations, such as main-belt asteroidsor individual asteroid families, in more detail. Shape models can also be used in combination with other types of observational data (IR, adaptive optics images, stellar occultations), e.g., to determine sizes and thermal properties. We use all available photometric data of asteroidsto derive their physical models by the lightcurve inversion method and compare the observed pole latitude distributions of all asteroidswith known convex shape models with the simulated pole latitude distributions. We used classical dense photometric lightcurves from several sources and sparse-in-time photometry from the U.S. Naval Observatory in Flagstaff, Catalina Sky Survey, and La Palma surveys (IAU codes 689, 703, 950) in the lightcurve inversion method to determine asteroidconvex models and their rotational states. We also extended a simple dynamical model for the spin evolution of asteroidsused in our previous paper. We present 119 new asteroidmodels derived from combined dense and sparse-in-time photometry. We discuss the reliability of asteroidshape models derived only from Catalina Sky Survey data (IAU code 703) and present 20 such models. By using different values for a scaling parameter cYORP (corresponds to the magnitude of the YORP momentum) in the dynamical model for the spin evolution and by comparing synthetics and observed pole-latitude distributions, we were able to constrain the typical values of the cYORP parameter as between 0.05 and 0.6.
    • Correction
    • Source
    • Cite
    • Save
    62
    References
    80
    Citations
    NaN
    KQI
    []
    Baidu
    map