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, 507(1) pp. 1546–1556.

DOI: https://doi.org/10.1093/mnras/stab2195

Abstract

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.

Viewing alternatives

Download history

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About

Recommendations