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Radio Galaxy Zoo: machine learning for radio source host galaxy cross-identification

Citation

Alger, MJ and Banfield, JK and Ong, CS and Rudnick, L and Wong, OI and Wolf, C and Andernach, H and Norris, RP and Shabala, SS, Radio Galaxy Zoo: machine learning for radio source host galaxy cross-identification, Monthly Notices of the Royal Astronomical Society, 478, (4) pp. 5547-5563. ISSN 0035-8711 (2018) [Refereed Article]


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Copyright Statement

Copyright 2018 The Authors. This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society :2018. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.

DOI: doi:10.1093/mnras/sty1308

Abstract

We consider the problem of determining the host galaxies of radio sources by cross-identification. This has traditionally been done manually, which will be intractable for wide-area radio surveys like the Evolutionary Map of the Universe. Automated cross-identification will be critical for these future surveys, and machine learning may provide the tools to develop such methods. We apply a standard approach from computer vision to cross-identification, introducing one possible way of automating this problem, and explore the pros and cons of this approach. We apply our method to the 1.4 GHz Australian Telescope Large Area Survey (ATLAS) observations of the Chandra Deep Field South (CDFS) and the ESO Large Area ISO Survey South 1 fields by cross-identifying them with the Spitzer Wide-area Infrared Extragalactic survey. We train our method with two sets of data: expert cross-identifications of CDFS from the initial ATLAS data release and crowdsourced cross-identifications of CDFS from Radio Galaxy Zoo. We found that a simple strategy of cross-identifying a radio component with the nearest galaxy performs comparably to our more complex methods, though our estimated best-case performance is near 100 per cent. ATLAS contains 87 complex radio sources that have been cross-identified by experts, so there are not enough complex examples to learn how to cross-identify them accurately. Much larger data sets are therefore required for training methods like ours. We also show that training our method on Radio Galaxy Zoo cross-identifications gives comparable results to training on expert cross-identifications, demonstrating the value of crowdsourced training data.

Item Details

Item Type:Refereed Article
Keywords:methods: statistical, techniques: miscellaneous, galaxies: active, infrared: galaxies, radio continuum: galaxies
Research Division:Physical Sciences
Research Group:Astronomical and Space Sciences
Research Field:Cosmology and Extragalactic Astronomy
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Physical Sciences
UTAS Author:Shabala, SS (Dr Stas Shabala)
ID Code:130388
Year Published:2018
Funding Support:Australian Research Council (DE130101399)
Web of Science® Times Cited:9
Deposited By:Mathematics and Physics
Deposited On:2019-01-23
Last Modified:2019-03-13
Downloads:22 View Download Statistics

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