Estimation of Differential Photometry in Adaptive Optics Observations with a Wavelet-based Maximum Likelihood Estimator

Details Estimation of Differential Photometry in Adaptive Optics Observations with a Wavelet-based Maximum Likelihood Estimator Estimation of Differential Photometry in Adaptive Optics Observations with a Wavelet-based Maximum Likelihood Estimator

Jul 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011pasp..123..865b&link_type=abstract

Publications of the Astronomical Society of the Pacific, Volume 123, issue 905, pp.865-878

Physics

Optics

Data Analysis And Techniques

Scientific paper

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 reference 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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