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

Nicholas O. Ralph, Ray P. Norris, Gu Fang, Laurence A.F. Park, Timothy J. Galvin, Matthew J. Alger, Heinz Andernach, Chris Lintott, Lawrence Rudnick, Stanislav Shabala, O. Ivy Wong

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Article number108011
JournalPublications of the Astronomical Society of the Pacific
Volume131
Issue number1004
DOIs
Publication statusPublished - 1 Oct 2019

Fingerprint

zoo
organizing
radio galaxies
machine learning
education
radio
outlier
method

Cite this

Ralph, Nicholas O. ; Norris, Ray P. ; Fang, Gu ; Park, Laurence A.F. ; Galvin, Timothy J. ; Alger, Matthew J. ; Andernach, Heinz ; Lintott, Chris ; Rudnick, Lawrence ; Shabala, Stanislav ; Wong, O. Ivy. / Radio galaxy zoo : Unsupervised clustering of convolutionally auto-encoded radio-astronomical images. In: Publications of the Astronomical Society of the Pacific. 2019 ; Vol. 131, No. 1004.
@article{bffc13e00bff45918e2b170ae6419301,
title = "Radio galaxy zoo: Unsupervised clustering of convolutionally auto-encoded radio-astronomical images",
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.",
keywords = "Astronomical databases: miscellaneous, Methods: data analysis, Radio continuum: galaxies, Surveys",
author = "Ralph, {Nicholas O.} and Norris, {Ray P.} and Gu Fang and Park, {Laurence A.F.} and Galvin, {Timothy J.} and Alger, {Matthew J.} and Heinz Andernach and Chris Lintott and Lawrence Rudnick and Stanislav Shabala and Wong, {O. Ivy}",
year = "2019",
month = "10",
day = "1",
doi = "10.1088/1538-3873/ab213d",
language = "English",
volume = "131",
journal = "Publications of the Astronomical Society of the Pacific",
issn = "0004-6280",
publisher = "University of Chicago",
number = "1004",

}

Ralph, NO, Norris, RP, Fang, G, Park, LAF, Galvin, TJ, Alger, MJ, Andernach, H, Lintott, C, Rudnick, L, Shabala, S & 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, 108011. https://doi.org/10.1088/1538-3873/ab213d

Radio galaxy zoo : Unsupervised clustering of convolutionally auto-encoded radio-astronomical images. / Ralph, Nicholas O.; Norris, Ray P.; Fang, Gu; Park, Laurence A.F.; Galvin, Timothy J.; Alger, Matthew J.; Andernach, Heinz; Lintott, Chris; Rudnick, Lawrence; Shabala, Stanislav; Wong, O. Ivy.

In: Publications of the Astronomical Society of the Pacific, Vol. 131, No. 1004, 108011, 01.10.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Radio galaxy zoo

T2 - Unsupervised clustering of convolutionally auto-encoded radio-astronomical images

AU - Ralph, Nicholas O.

AU - Norris, Ray P.

AU - Fang, Gu

AU - Park, Laurence A.F.

AU - Galvin, Timothy J.

AU - Alger, Matthew J.

AU - Andernach, Heinz

AU - Lintott, Chris

AU - Rudnick, Lawrence

AU - Shabala, Stanislav

AU - Wong, O. Ivy

PY - 2019/10/1

Y1 - 2019/10/1

N2 - 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.

AB - 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.

KW - Astronomical databases: miscellaneous

KW - Methods: data analysis

KW - Radio continuum: galaxies

KW - Surveys

UR - http://www.scopus.com/inward/record.url?scp=85073556697&partnerID=8YFLogxK

U2 - 10.1088/1538-3873/ab213d

DO - 10.1088/1538-3873/ab213d

M3 - Article

VL - 131

JO - Publications of the Astronomical Society of the Pacific

JF - Publications of the Astronomical Society of the Pacific

SN - 0004-6280

IS - 1004

M1 - 108011

ER -