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Radio galaxy zoo: Knowledge transfer using rotationally invariant self-organizing maps


Galvin, TJ and Huynh, M and Norris, RP and Wang, XR and Hopkins, E and Wong, OI and Shabala, S and Rudnick, L and Alger, MJ and Polsterer, KL, Radio galaxy zoo: Knowledge transfer using rotationally invariant self-organizing maps, Publications of the Astronomical Society of the Pacific, 131, (1004) Article 108009. ISSN 0004-6280 (2019) [Refereed Article]

Copyright Statement

2019. The Astronomical Society of the Pacific

DOI: doi:10.1088/1538-3873/ab150b


With the advent of large scale-surveys the manual analysis and classification of individual radio source morphologies is rendered impossible as existing approaches do not scale. The analysis of complex morphological features in the spatial domain is a particularly important task. Here, we discuss the challenges of transferring crowdsourced labels obtained from the Radio Galaxy Zoo project and introduce a proper transfer mechanism via quantile random forest regression. By using parallelized rotation and flipping invariant Kohonen-maps, image cubes of Radio Galaxy Zoo selected galaxies formed from the Faint Images of the Radio Sky at Twenty-cm (FIRST) radio continuum and the Wide-field Infrared Survey Explorer (WISE) infrared all-sky surveys are first projected down to a two-dimensional embedding in an unsupervised way. This embedding can be seen as a discretized space of shapes with the coordinates reflecting morphological features as expressed by the automatically derived prototypes. We find that these prototypes have reconstructed physically meaningful processes across two channel images at radio and infrared wavelengths in an unsupervised manner. In the second step, images are compared with those prototypes to create a heat map, which is the morphological fingerprint of each object and the basis for transferring the user generated labels. These heat maps have reduced the feature space by a factor of 248, and are able to be used as the basis for subsequent machine-learning (ML) methods. Using an ensemble of decision trees we achieve upwards of 85.7% and 80.7% accuracy when predicting the number of components and peaks in an image, respectively, using these heat maps. We also question the currently used discrete classification schema and introduce a continuous scale that better reflects the uncertainty in transition between two classes, caused by sensitivity and resolution limits.

Item Details

Item Type:Refereed Article
Keywords:galaxies: general galaxies: jets galaxies: statistics radio continuum: general infrared: general
Research Division:Physical Sciences
Research Group:Astronomical 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, S (Associate Professor Stas Shabala)
ID Code:136809
Year Published:2019
Web of Science® Times Cited:5
Deposited By:Physics
Deposited On:2020-01-20
Last Modified:2020-08-19

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