  The capability of the Terrestrial Planet Finder Interferometer (TPF-I) for
planetary signal extraction, including both detection and spectral
characterization, can be optimized by taking proper account of instrumental
characteristics and astrophysical prior information. We have developed the
Point Process Algorithm (PPA), a Bayesian technique for extracting planetary
signals using the sine-chopped outputs of a dual nulling interferometer. It is
so-called because it represents the system being observed as a set of points in
a suitably-defined state space, thus providing a natural way of incorporating
our prior knowledge of the compact nature of the targets of interest. It can
also incorporate the spatial covariance of the exozodi as prior information
which could help mitigate against false detections. Data at multiple
wavelengths are used simultaneously, taking into account possible spectral
variations of the planetary signals. Input parameters include the RMS
measurement noise and the a priori probability of the presence of a planet. The
output can be represented as an image of the intensity distribution on the sky,
optimized for the detection of point sources. Previous approaches by others to
the problem of planet detection for TPF-I have relied on the potentially
non-robust identification of peaks in a "dirty" image, usually a correlation
map. Tests with synthetic data suggest that the PPA provides greater
sensitivity to faint sources than does the standard approach (correlation map +
CLEAN), and will be a useful tool for optimizing the design of TPF-I.
