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The capability of the Terrestrial Planet Finder Interferometer (TPF-I) forplanetary signal extraction, including both detection and spectralcharacterization, can be optimized by taking proper account of instrumentalcharacteristics and astrophysical prior information. We have developed thePoint Process Algorithm (PPA), a Bayesian technique for extracting planetarysignals using the sine-chopped outputs of a dual nulling interferometer. It isso-called because it represents the system being observed as a set of points ina suitably-defined state space, thus providing a natural way of incorporatingour prior knowledge of the compact nature of the targets of interest. It canalso incorporate the spatial covariance of the exozodi as prior informationwhich could help mitigate against false detections. Data at multiplewavelengths are used simultaneously, taking into account possible spectralvariations of the planetary signals. Input parameters include the RMSmeasurement noise and the a priori probability of the presence of a planet. Theoutput can be represented as an image of the intensity distribution on the sky,optimized for the detection of point sources. Previous approaches by others tothe problem of planet detection for TPF-I have relied on the potentiallynon-robust identification of peaks in a "dirty" image, usually a correlationmap. Tests with synthetic data suggest that the PPA provides greatersensitivity to faint sources than does the standard approach (correlation map +CLEAN), and will be a useful tool for optimizing the design of TPF-I.Comment: 17 pages, 6 figures. AJ in press (scheduled for Nov 2006

Point Process Algorithm: A New Bayesian Approach for Planet Signal Extraction with theTerrestrial Planet Finder-Interferometer