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A deconvolution-based algorithm for crowded field photometry with unknown Point Spread Function

P. Magain·F. Courbin·M. Gillon·S. Sohy·G. Letawe·V. Chantry·Y. Letawe
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Abstract

A new method is presented for determining the Point Spread Function (PSF) of images that lack bright and isolated stars. It is based on the same principles as the MCS (Magain, Courbin, Sohy, 1998) image deconvolution algorithm. It uses the information contained in all stellar images to achieve the double task of reconstructing the PSFs for single or multiple exposures of the same field and to extract the photometry of all point sources in the field of view. The use of the full information available allows to construct an accurate PSF. The possibility to simultaneously consider several exposures makes it very well suited to the measurement of the light curves of blended point sources from data that would be very difficult or even impossible to analyse with traditional PSF fitting techniques. The potential of the method for the analysis of ground-based and space-based data is tested on artificial images and illustrated by several examples, including HST/NICMOS images of a lensed quasar and VLT/ISAAC images of a faint blended Mira star in the halo of the giant elliptical galaxy NGC5128 (Cen A).

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P. Magain, F. Courbin, M. Gillon, S. Sohy, G. Letawe, V. Chantry, Y. Letawe. 2006-09-21. A deconvolution-based algorithm for crowded field photometry with unknown Point Spread Function. https://doi.org/10.1051/0004-6361%3A20042505

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