Stochastic characterization of long-term NEO dynamics

The propagation of Near-Earth Object (NEO) orbits are sensitive to initial conditions due to the presence of planetary close encounters. Thus, the determination of their orbits beyond a few centuries is very challenging. In order to understand the evolution of the orbit of NEOs over millions of years their orbits must be studied statistically.

The frequency of close encounters drives the stochastic nature of the orbital evolution of near-Earth objects. To efficiently model these effects we have developed a semi-analytical propagation tool that combines the two driving dynamical effects in the propagation of NEOs; first, the detection and evaluation of close planetary encounters; second, in the absence of planetary close encounters, the secular perturbation from Jupiter. The tool provides a decrease in runtime of 2-3 orders of magnitude in long-term simulations. Further, since the tool accurately captures the qualitative nature of the dynamics, it can be used to develop statistical characterizations of the long-term orbital evolution of NEO.

We study the long-term dynamics of NEOs by sampling a large number of virtual asteroids in the vicinity of a nominal orbit of interest. The observed dynamics are characterized by a random walk in semi-major axis, eccentricity and inclination. On the other hand, the distributions in ascending node and argument of perihelion become uniformly distributed after a few hundred thousand years. In this presentation we analyze multiple regions of near-Earth space and study the characteristic timescales of the stochastic processes that drive the long-term evolution of NEO orbits.

The obliquity of asteroids plays a role in their long-term dynamics through the Yarkovsky and YORP thermal effects. As a result of the stochastic evolution of the orbit of asteroids, their obliquity also evolves stochastically. We characterize this evolution and analyze the significance of this dispersion in the prediction of their long-term non-gravitational dynamics.