Super-resolving Herschel imaging: a proof of concept using Deep Neural Networks

Lauritsen, Lynge; Dickinson, Hugh; Bromley, Jane; Serjeant, Stephen; Lim, Chen-Fatt; Gao, Zhen-Kai and Wang, Wei-Hao (2021). Super-resolving Herschel imaging: a proof of concept using Deep Neural Networks. Monthly Notices of the Royal Astronomical Society, article no. stab2195.



Wide-field sub-millimetre 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 autoencoder with a novel loss function to overcome this problem in the sub-millimeter wavelength range. This approach is successfully demonstrated on Herschel SPIRE 500μm COSMOS data, with the super-resolving target being the JCMT SCUBA-2 450μm observations of the same field. We reproduce the JCMT SCUBA-2 images with high fidelity using this autoencoder. This is quantified through the point source fluxes and positions, the completeness and the purity.

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