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We present a new, fully generative model of optical telescope image sets,along with a variational procedure for inference. Each pixel intensity istreated as a Poisson random variable, with a rate parameter dependent on latentproperties of stars and galaxies. Key latent properties are themselves random,with scientific prior distributions constructed from large ancillary data sets.We check our approach on synthetic images. We also run it on images from amajor sky survey, where it exceeds the performance of the currentstate-of-the-art method for locating celestial bodies and measuring theircolors.

Celeste: Variational inference for a generative model of astronomical images