Abstract
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor. ABSTRACT. We propose to use the Bayesian framework and the wavelet transform (WT) to estimate differential photometry in binary systems imaged with adaptive optics (AO). We challenge the notion that Richardson-Lucy-type algorithms are not suitable to AO observations because of the mismatch between the target's and ref-erence star's point-spread functions (PSFs). Using real data obtained with the Lick Observatory AO system on the 3 m Shane telescope, we first obtain a deconvolved image by means of the Adaptive Wavelets Maximum Likelihood Estimator (AWMLE) approach. The algorithm reconstructs an image that maximizes the compound Poisson and Gaussian likelihood of the data. It also performs wavelet decomposition, which helps to distinguish signal from noise, and therefore it aides the stopping rule. We test photometric precision of that approach versus PSF-fitting with the StarFinder package for companions located within the halo created by the bright star. Simultaneously, we test the susceptibility of both approaches to error in the reference PSF, as quantified by the difference in the Strehl ratio between the science and calibration PSFs. We show that AWMLE is capable of producing better results than PSF-fitting. More importantly, we have developed a methodology for testing photometric codes for AO observations.
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CITATION STYLE
Baena Gallé, R., & Gladysz, S. (2011). Estimation of Differential Photometry in Adaptive Optics Observations with a Wavelet-based Maximum Likelihood Estimator. Publications of the Astronomical Society of the Pacific, 123(905), 865–878. https://doi.org/10.1086/661186
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