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Near-future large galaxy surveys will encounter blended galaxy images at afraction of up to 50% in the densest regions of the universe. Currentdeblending techniques may segment the foreground galaxy while leaving missingpixel intensities in the background galaxy flux. The problem is compounded bythe diffuse nature of galaxies in their outer regions, making segmentationsignificantly more difficult than in traditional object segmentationapplications. We propose a novel branched generative adversarial network (GAN)to deblend overlapping galaxies, where the two branches produce images of thetwo deblended galaxies. We show that generative models are a powerful enginefor deblending given their innate ability to infill missing pixel valuesoccluded by the superposition. We maintain high peak signal-to-noise ratio andstructural similarity scores with respect to ground truth images upondeblending. Our model also predicts near-instantaneously, making it a naturalchoice for the immense quantities of data soon to be created by large surveyssuch as LSST, Euclid and WFIRST.

Deblending galaxy superpositions with branched generative adversarial networks