Machine learning techniques have been increasingly useful in astronomical applications overthe last few years, for example in the morphological classification of galaxies. Convolutionalneural networks have proven to be highly effective in classifying objects in image data. Inthe context of radio-interferometric imaging in astronomy, we looked for ways to identifymultiple components of individual sources. To this effect, we design a convolutional neuralnetwork to differentiate between different morphology classes using sources from the RadioGalaxy Zoo (RGZ) citizen science project. In this first step, we focus on exploring the factorsthat affect the performance of such neural networks, such as the amount of training data, number and nature of layers, and the hyperparameters. We begin with a simple experiment inwhich we only differentiate between two extreme morphologies, using compact and multiplecomponentextended sources. We found that a three-convolutional layer architecture yieldedvery good results, achieving a classification accuracy of 97.4 per cent on a test data set. The same architecture was then tested on a four-class problem where we let the networkclassify sources into compact and three classes of extended sources, achieving a test accuracyof 93.5 per cent. The best-performing convolutional neural network set-up has been verifiedagainst RGZ Data Release 1 where a final test accuracy of 94.8 per cent was obtained, usingboth original and augmented images. The use of sigma clipping does not offer a significantbenefit overall, except in cases with a small number of training images.
Bibliographical noteFunding Information:
This publication has been made possible by the participation of more than 10 000 volunteers in the Radio Galaxy Zoo project. The data in this paper are the results of the efforts of the Radio Galaxy Zoo volunteers, without whom none of this work would be possible. Their efforts are individually acknowledged at http://rgzauthors.galaxyzoo.org. JKB acknowledges financial support from the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020.
VL acknowledges support by the Deutsche Forschungsgemein-schaft (DFG) through grant SFB 676.
Partial support for this work at the University of Minnesota comes from grants AST-1211595 and AST-1714205 from the US National Science Foundation. The FIRST survey was conducted on the Very Large Array of the National Radio Astronomy Observatory, which is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.
BS gratefully acknowledges support from Balliol College, Oxford, through the Henry Skynner Junior Research Fellowship and from the National Aeronautics and Space Administration (NASA) through Einstein Post-doctoral Fellowship Award Number PF5-160143 issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of NASA under contract NAS8-03060.
© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.
- Instrumentation: miscellaneous
- Methods: miscellaneous
- Radio continuum: galaxies
- Techniques: miscellaneous