Superresolving Herschel imaging: A proof of concept using Deep Neural Networks

Abstract

Wide-field submillimetre surveys have driven many major advances in galaxy evolution in the past decade, but without extensive follow-up observations the coarse angular resolution of these surveys limits the science exploitation. This has driven the development of various analytical deconvolution methods. In the last half a decade Generative Adversarial Networks have been used to attempt deconvolutions on optical data. Here, we present an auto-encoder with a novel loss function to overcome this problem in the submillimeter wavelength range. This approach is successfully demonstrated on Herschel SPIRE 500 μm COSMOS data, with the superresolving target being the JCMT SCUBA-2 450 μm observations of the same field. We reproduce the JCMT SCUBA-2 images with high fidelity using this auto-encoder. This is quantified through the point source fluxes and positions, the completeness, and the purity.