Point Spread Function extraction in crowdedelds using blind deconvolution

The extraction of the Point Spread Function (PSF) from astronomical data is an important issue for data reduction packages for stellar photometry that use PSF tting. High resolution Adaptive Optics images are characterized by a highly structured PSF that cannot be represented by any simple analytical model. Even a numerical PSF extracted from the frame can be aected by the eld crowding eects. In this paper we use blind deconvolution in order to nd an approximation of both the unknown object and the unknown PSF. In particular we adopt an iterative inexact alternating minimization method where each iteration (that we called outer iteration) consists in alternating an update of the object and of the PSF by means of xed numbers of (inner) iterations of the Scaled Gradient Projection (SGP) method. The use of SGP allows the introduction of dierent constraints on the object and on the PSF. In particular, we introduce a constraint on the PSF which is an upper bound derived from the Strehl ratio (SR), to be provided together with the input data. In this contribution we show the photometric error dependence on the crowding, having simulated images generated with synthetic PSFs available from the Phase-A study of the E-ELT MCAO system (MAORY) and dierent crowding conditions.