Statistics – Computation
Scientific paper
Nov 2001
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001dda....32.1104g&link_type=abstract
American Astronomical Society, DDA Meeting #32, #11.04; Bulletin of the American Astronomical Society, Vol. 33, p.1197
Statistics
Computation
Scientific paper
In a previous work (Grazier et. al), we examined the long-term stability of planetesimal orbits in the gaps between the jovian planets. Using a high-order modified Störmer multistep integrator, we integrated the trajectories of 100,000 particles in the Jupiter-Saturn gap, and 10,000 particles in both the Saturn-Uranus and Uranus-Neptune gaps for up to 1 Gy. The goal was to search for stable orbits in which primordial planetesimal material may still reside. Planet/Planetesimal close approaches were not simulated, and the spheres of influence treated as ``hard spheres" -- any particle entering a sphere was removed from the simulation. We now return to the zones between the jovian planets, simulating the trajectories of 10,000 massless particles in each of the three gaps for durations up to 100 Ky. In our current simulations, we simulate close planet/planetesimal encounters, and store information from each close encounter in a massive database. We can track the history---in orbital element space---of every particle, as well as re-integrate any single close encounter. Tracking the history of the planetesimal swarm allows us to examine the relative number of planetesimals which migrate to the Kuiper Belt and Oort Cloud, versus the number which deliver cometary material to the inner Solar System. By re-integrating each close approach accurately, adding a randomly-assigned mass to the planetesimal, we examine the effect of planetesimal mass, encounter velocity, encounter duration, and minimum encounter distance on the energy and angular momentum of the planet. This has important implications for planet migration in the early solar nebula. This research was funded in part by the Jet Propulsion Laboratory, California Institute of Technolgy (KRG), NSF Grant AST96-19574 (FV), as well as a grant of computational resources from Sun Microsystems, Inc.
Grazier Kevin R.
Varadi Ferenc
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