Multi-channel 2D photometry with super-resolution in far UV astronomical images using priors in visible bands

Details Multi-channel 2D photometry with super-resolution in far UV astronomical images using priors in visible bands Multi-channel 2D photometry with super-resolution in far UV astronomical images using priors in visible bands

Mar 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008spie.6812e..47l&link_type=abstract

Image Processing: Algorithms and Systems VI. Edited by Astola, Jaakko T.; Egiazarian, Karen O.; Dougherty, Edward R. Proceed

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Scientific paper

Photometry of crowded fields is an old theme of astronomical image processing. Large space surveys in the UV (ultraviolet), like the GALEX mission (135-175 nm and 170-275 nm range), confronts us again with challenges like, very low light levels, poor resolution, variable stray-light in background, the extended and badly known PSFs (point spread functions), etc. However the morphological similitude of these UV images to their counterparts in the visible bands, suggests that we use all this high resolution data as the starting reference for the UV analysis. We choose the Bayesian approach. However there is not a straightforward way leading from the basic idea to its practical implementation. We will describe in this paper the path which starts with the original procedure (presented in a previous paper) and ends on the useful one. After a brief recall on the Bayesian method, we describe the process applied to restore from the UV images the point spread function (PSF) and the background due to stray-light. In the end we display the photometric performances reached for each channel and we discuss the consequences of the imperfect knowledge of background, the inaccuracy on object centring and on the PSF model. Results show a clear improvement by more than 2 mag on the magnitude limit and in the completeness of the measured objects relative to classical methods (it corresponds to more than 75000 new objects per GALEX field, i.e. approx 25% more). The simplicity of the Bayesian approach eased the analysis as well as the corrections needed in order to obtain a useful and reliable photometric procedure.

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