Superresolving Herschel imaging: a proof of concept using Deep Neural Networks. Issue 1 (30th July 2021)

• Record Type: Journal Article
• Authors: Lauritsen, Lynge; Dickinson, Hugh; Bromley, Jane; Serjeant, Stephen; Lim, Chen-Fatt; Gao, Zhen-Kai; Wang, Wei-Hao
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 507:Issue 1(2021)
• Page Start: 1546
• Page End: 1556
• Publication Date: 2021-07-30
• Subjects: methods: data analysis -- submillimetre: galaxies; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stab2195
• Languages: English
• ISSNs: 0035-8711

Abstract

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 $\mu\mathrm{m}$ COSMOS data, with the superresolving target being the JCMT SCUBA-2 450 $\mu\mathrm{m}$ 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.