Photometry in UV astronomical images of extended sources in crowded field using deblended images in optical visible bands as Bayesian priors

Details Photometry in UV astronomical images of extended sources in crowded field using deblended images in optical visible bands as Bayesian priors Photometry in UV astronomical images of extended sources in crowded field using deblended images in optical visible bands as Bayesian priors

Feb 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009spie.7246e..23v&link_type=abstract

Computational Imaging VII. Edited by Bouman, Charles A.; Miller, Eric L.; Pollak, Ilya. Proceedings of the SPIE, Volume 7246 (2

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Photometry of astrophysical sources, galaxies and stars, in crowded field images, if an old problem, is still a challenging goal, as new space survey missions are launched, releasing new data with increased sensibility, resolution and field of view. The GALEX mission, observes in two UV bands and produces deep sky images of millions of galaxies or stars mixed together. These UV observations are of lower resolution than same field observed in visible bands, and with a very faint signal, at the level of the photon noise for a substantial fraction of objects. Our purpose is to use the better known optical counterparts as prior information in a Bayesian approach to deduce the UV flux. Photometry of extended sources has been addressed several times using various techniques: background determination via sigma clipping, adaptative-aperture, point-spread-function photometry, isophotal photometry, to lists some. The Bayesian approach of using optical priors for solving the UV photometry has already been applied by our team in a previous work. Here we describe the improvement of using the extended shape inferred by deblending the high resolution optical images and not only the position of the optical sources. The resulting photometric accuracy has been tested with simulation of crowded UV fields added on top of real UV images. Finally, this helps to converge to smaller and flat residual and increase the faint source detection threshold. It thus gives the opportunity to work on 2nd order effects, like improving the knowledge of the background or point-spread function by iterating on them.

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