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Radio galaxy zoo: Unsupervised clustering of convolutionally auto-encoded radio-astronomical images

Ralph, NO, Norris, RP, Fang, G, Park, LAF, Galvin, TJ, Alger, MJ, Andernach, H, Lintott, C, Rudnick, L, Shabala, S ORCID: 0000-0001-5064-0493 and Wong, OI 2019 , 'Radio galaxy zoo: Unsupervised clustering of convolutionally auto-encoded radio-astronomical images' , Publications of the Astronomical Society of the Pacific, vol. 131, no. 1004 , pp. 1-17 , doi: 10.1088/1538-3873/ab213d.

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Abstract

This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a self-organizing map (SOM) and a convolutional autoencoder. The rapidly increasing volume of radio-astronomical data has increased demand for machine-learning methods as solutions to classification and outlier detection. Major astronomical discoveries are unplanned and found in the unexpected, making unsupervised machine learning highly desirable by operating without assumptions and labeled training data. Our approach shows SOM training time is drastically reduced and high-level features can be clustered by training on auto-encoded feature vectors instead of raw images. Our results demonstrate this method is capable of accurately separating outliers on a SOM with neighborhood similarity and K-means clustering of radio-astronomical features. We present this method as a powerful new approach to data exploration by providing a detailed understanding of the morphology and relationships of Radio Galaxy Zoo (RGZ) data set image features which can be applied to new radio survey data.

Item Type: Article
Authors/Creators:Ralph, NO and Norris, RP and Fang, G and Park, LAF and Galvin, TJ and Alger, MJ and Andernach, H and Lintott, C and Rudnick, L and Shabala, S and Wong, OI
Keywords: astronomical databases: miscellaneous – radio continuum: galaxies – methods: data analysis – surveys
Journal or Publication Title: Publications of the Astronomical Society of the Pacific
Publisher: Univ Chicago Press
ISSN: 0004-6280
DOI / ID Number: 10.1088/1538-3873/ab213d
Copyright Information:

© 2019. The Astronomical Society of the Pacific

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