Title: Deblending galaxy superpositions with branched generative adversarial networks

Abstract

Abstract Near-future large galaxy surveys will encounter blended galaxy images at a fraction of up to 50 per cent in the densest regions of the Universe. Current deblending techniques may segment the foreground galaxy while leaving missing pixel intensities in the background galaxy flux. The problem is compounded by the diffuse nature of galaxies in their outer regions, making segmentation significantly more difficult than in traditional object segmentation applications. We propose a novel branched generative adversarial network to deblend overlapping galaxies, where the two branches produce images of the two deblended galaxies. We show that generative models are a powerful engine for deblending given their innate ability to infill missing pixel values occluded by the superposition. We maintain high peak signal-to-noise ratio and structural similarity scores with respect to ground truth images upon deblending. Our model also predicts near-instantaneously, making it a natural choice for the immense quantities of data soon to be created by large surveys such as Large Synoptic Survey Telescope, Euclid, and Wide-Field Infrared Survey Telescope.

Authors:

Reiman, David M. ^[1]; Göhre, Brett E. ^[1]

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+ Show Author Affiliations

1. Department of Physics, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA

Publication Date:

Wed Feb 27 00:00:00 EST 2019

Sponsoring Org.:

USDOE

OSTI Identifier:

1499070

Resource Type:

Published Article

Journal Name:

Monthly Notices of the Royal Astronomical Society

Additional Journal Information:

Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 485 Journal Issue: 2; Journal ID: ISSN 0035-8711

Publisher:

Oxford University Press

Country of Publication:

United Kingdom

Language:

English