  High-precision radial velocity planet searches have surveyed over ~2000
nearby stars and detected over ~200 planets. While these same stars likely
harbor many additional planets, they will become increasingly challenging to
detect, as they tend to have relatively small masses and/or relatively long
orbital periods. Therefore, observers are increasing the precision of their
observations, continuing to monitor stars over decade timescales, and also
preparing to survey thousands more stars. Given the considerable amounts of
telescope time required for such observing programs, it is important use the
available resources as efficiently as possible. Previous studies have found
that a wide range of predetermined scheduling algorithms result in planet
searches with similar sensitivities. We have developed adaptive scheduling
algorithms which have a solid basis in Bayesian inference and information
theory and also are computationally feasible for modern planet searches. We
have performed Monte Carlo simulations of plausible planet searches to test the
power of adaptive scheduling algorithms. Our simulations demonstrate that
planet searches performed with adaptive scheduling algorithms can
simultaneously detect more planets, detect less massive planets, and measure
orbital parameters more accurately than comparable surveys using a non-adaptive
scheduling algorithm. We expect that these techniques will be particularly
valuable for the N2K radial velocity planet search for short-period planets as
well as future astrometric planet searches with the Space Interferometry
Mission which aim to detect terrestrial mass planets.
