No abstract
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Direct detection of extrasolar Jovian planets is a major scientific motivation for the construction of future extremely large telescopes such as the Thirty Meter Telescope (TMT). Such detection will require dedicated high-contrast AO systems. Since the properties of Jovian planets and their parent stars vary enormously between different populations, the instrument must be designed to meet specific scientific needs rather than a simple metric such as maximum Strehl ratio. We present a design for such an instrument, the Planet Formation Imager (PFI) for TMT. It has four key science missions. The first is the study of newly-formed planets on 5-10 AU scales in regions such as Taurus and Ophiucusthis requires very small inner working distances that are only possible with a 30m or larger telescope. The second is a robust census of extrasolar giant planets orbiting mature nearby stars. The third is detailed spectral characterization of the brightest extrasolar planets. The final targets are circumstellar dust disks, including Zodiacal light analogs in the inner parts of other solar systems. To achieve these, PFI combines advanced wavefront sensors, high-order MEMS deformable mirrors, a coronagraph optimized for a finely-segmented primary mirror, and an integral field spectrograph.
Two completely independent systematic approaches for designing algorithms are presented. One approach uses recursion rules to generate a new algorithm from an old one, only with an insensitivity to more error sources. The other approach uses a least-squares method to optimize the noise performance of an algorithm while constraining it to a desired set of properties. These properties might include insensitivity to detector nonlinearities as high as a certain power, insensitivity to linearly varying laser power, and insensitivity to some order to the piezoelectric transducer voltage ramp with the wrong slope. A noise figure of merit that is valid for any algorithm is also derived. This is crucial for evaluating algorithms and is what is maximized in the least-squares method. This noise figure of merit is a certain average over the phase because in general the noise sensitivity depends on it. It is valid for both quantization noise and photon noise. The equations that must be satisfied for an algorithm to be insensitive to various error sources are derived. A multivariate Taylor-series expansion in the distortions is used, and the time-varying background and signal amplitudes are expanded in Taylor series in time. Many new algorithms and families of algorithms are derived.
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