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Observational data from astronomical imaging surveys contain informationabout a variety of source populations and environments, and its complexity willincrease substantially as telescopes become more sensitive. Even for existingobservations, measuring the correlations between point-like and diffuseemission can be crucial to correctly inferring the properties of any individualcomponent. For this task information is typically lost, either because ofconservative data cuts, aggressive filtering or incomplete treatment ofcontaminated data. We present the code PCAT-DE, an extension of probabilisticcataloging designed to simultaneously model point-like and diffuse signals.This work incorporates both explicit spatial templates and a set ofnon-parametric Fourier component templates into a forward model of astronomicalimages, reducing the number of processing steps applied to the observed data.Using synthetic Herschel-SPIRE multiband observations, we demonstrate thatpoint source and diffuse emission can be reliably separated and measured. Wepresent two applications of this model. For the first, we perform point sourcedetection/photometry in the presence of galactic cirrus and demonstrate thatcosmic infrared background (CIB) galaxy counts can be recovered in cases ofsignificant contamination. In the second we show that the spatially extendedthermal Sunyaev-Zel'dovich (tSZ) effect signal can be reliably measured evenwhen it is subdominant to the point-like emission from individual galaxies.

PCAT-DE: Reconstructing point-like and diffuse signals in astronomical images using spatial and spectral information