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

T. J. Galvin, M. Huynh, R. P. Norris, X. R. Wang, E. Hopkins, O. I. Wong, S. Shabala, L. Rudnick, M. J. Alger, K. L. Polsterer

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number108009
JournalPublications of the Astronomical Society of the Pacific
Volume131
Issue number1004
DOIs
StatePublished - Oct 2019

Bibliographical note

Funding Information:
This publication makes use of data products from the Wide- field Infrared Survey Explorer and the Spitzer Space Telescope. The Wide-field Infrared Survey Explorer is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. SWIRE is supported by NASA through the SIRTF Legacy Program under contract 1407 with the Jet Propulsion Laboratory. This publication makes use of radio data from the Australia Telescope Compact Array and the Karl G. Jansky Very Large Array (operated by NRAO). The Australia Telescope Compact Array is part of the Australia Telescope, which is funded by the Commonwealth of Australia for operation as a National Facility managed by CSIRO. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.

Funding Information:
Partial support for L.R. comes from U.S. National Science Foundation grant AST17-14205 to the University of Minnesota.

Funding Information:
K.P. and E.H. gratefully acknowledge the support of the Klaus Tschira Foundation.

Publisher Copyright:
© 2019. The Astronomical Society of the Pacific.

Keywords

  • Galaxies: general
  • Galaxies: jets
  • Galaxies: statistics
  • Infrared: general
  • Radio continuum: general

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