Separating diffuse from point-like sources - a Bayesian approach
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We present the starblade algorithm, a method to separate superimposed point sources from auto-correlated, diffuse flux using a Bayesian model. Point sources are assumed to be independent from each other and to follow a power-law brightness distribution. The diffuse emission is modeled by a nonparametric lognormal model with a priori unknown correlation structure. This model enforces positivity of the underlying emission and allows for variation in the order of its magnitudes. The correlation structure is recovered non-parametrically as well with the diffuse flux and used for the separation of the point sources. Additionally many measurement artifacts appear as point-like or quasi-point-like effect, not compatible with superimposed diffuse emission. We demonstrate the capabilities of the derived method on synthetic data and data obtained by the Hubble Space Telescope, emphasizing its effect on instrumental effects as well as physical sources.
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