**AAS 196th Meeting, June 2000**

*Session 60. New Statistics for New Missions: Problems and Opportunities for Breakthrough Thinking*

Special Session Oral, Thursday, June 8, 2000, 2:00-3:30pm, Highland A/K
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## [60.02] Bayesian Methodology for the Space Interferometry Mission

*T. J. Loredo, D. F. Chernoff (Department of Astronomy, Cornell University)*

We will describe work in progress on the development of
Bayesian methodology for the analysis of data from the Space
Interferometry Mission (SIM). There are two main thrusts to
this work: development of new methods for the detection and
analysis of Keplerian reflex motion in astrometric data; and
adaptive experimental design for on-the-fly refinement of
the SIM grid.

For detection and measurement of reflex motions (e.g., from
planetary companions), we use the algorithm developed by
Bretthorst for the Bayesian analysis of superposed nonlinear
models to develop an alternative to the commonly used
Lomb-Scargle (LS) periodogram that we call the Kepler
periodogram. The LS periodogram emerges as a special case of
the Kepler periodogram when the data are 1-dimensional
(e.g., radial velocity (RV) measurements) and the bodies in
question are in a circular orbit. But the Kepler periodogram
generalizes the LS periodogram to account for orbital
eccentricity, higher dimensional data (e.g., astrometric
data, or a combination of astrometric and RV data), and
sources of systematic error such as uncertainty in inertial
motion.

We use the Bayesian theory of experimental design to develop
adaptive strategies for SIM observing. This includes
identifying the best sampling scheme for detecting and
monitoring Keplerian reflex motions in science targets, and
(perhaps more crucially) the adaptive refinement of the SIM
astrometric grid from observations of candidate grid stars
throughout the SIM mission. Included in this latter task are
classification of candidate grid objects as inertial or
noninertial and scheduling of observations to best update
our knowledge of grid star motions.

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