Handling a highly structured and spatially variable Point Spread Function in AO images


Laura Schreiber (1)(2) Emiliano Diolaiti (3) Michele Bellazzini (3) Paolo Ciliegi (3) Italo Foppiani (3) Laura Greggio (2) Barbara Lanzoni (1) Matteo Lombini (3)


(1) Università di Bologna – Dipartimento di Astronomia (2) INAF – Osservatorio Astronomico di Padova (3) INAF – Osservatorio Astronomico di Bologna


Adaptive Optics (AO) has become a key technology for all the main existing 8-meter class telescopes and is considered a kind of enabling technology for future Extremely Large Telescopes. AO systems increase the energy concentration of the Point Spread Function (PSF), but the PSF itself is also characterized by complex shape and spatial variation. Efforts in the AO PSF modeling and in the integration of suitable models in a code for image analysis are needed to improve the extraction of high-precision quantitative science from AO observations. The Starfinder code was one of the first full attempts to solve the problem of obtaining accurate photometry and astrometry from narrow field AO images with spatially constant and highly structured PSF. However it still lacks of suitable methods for handling spatially variable AO PSFs. Preliminary studies to fill this gap were made within the Starfinder code, modeling the AO PSF by a convolution of the best PSF in the field with a spatially dependent blurring function or by a fully analytic modeling of the PSF. However these attempts are limited to single specific cases. We are developing a set of models representative of the PSF shapes and variation across the imaged field that might be obtained from different AO systems. These models are based on observed data from present telescopes and on simulated images generated with synthetic PSFs available from the Phase-A study of the E-ELT MCAO system (MAORY). The study is supported by a theoretical analysis of AO systems properties through numerical simulations. This effort is part of a project aimed at upgrading the Starfinder code and provide it with a set of tools to handle spatially variable PSFs.

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