Photometry in Near and Far UV from GALEX Deep Surveys Using Optical Priors: Limits and Performances

摘要

Mission constraints in case of large astronomical photometric surveys in UV (like the GALEX deep survey) confront us with new challenges which result from poor resolution, low counting rates, etc. However, morphological similarity of these UV images to their optical counterparts is the basis for-a meaningful improvement on resolution. The method whose performances are described here uses visible data (catalog and image) as the starting reference point for the analysis in the far and near UV channels of GALEX. The unique point of our procedure is the Bayesian approach under the Poisson noise assumption. The solution is reached with a EM algorithm. Its photometric performance has been estimated by inserting randomly a large set of artificial stars into original UV images and measuring the whole as original images. This study shows that photometric performance depends on: 1) PSF accuracy, 2) background accuracy, 3) position accuracy and 4) catalog's precision.