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-Organising 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 labelled 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 neighbourhood similarity and K-means clustering of radio-astronomical features complexity. 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) dataset image features which can be applied to new radio survey data.

arXiv commentAccepted in Publications of the Astronomical Society of the Pacific, special issue on Machine Intelligence in Astronomy and Astrophysics. 23 pages, 8 full-page colour figures

References in corpus (16)

• Adam: A Method for Stochastic Optimization
• Scikit-learn: Machine Learning in Python
• An overview of gradient descent optimization algorithms
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• The LOFAR Two-metre Sky Survey - I. Survey Description and Preliminary Data Release
• EMU: Evolutionary Map of the Universe
• A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning
• Science With The Australian Square Kilometre Array Pathfinder
• Radio Galaxy Zoo: host galaxies and radio morphologies derived from visual inspection
• SKYNET: an efficient and robust neural network training tool for machine learning in astronomy
• Radio Galaxy Zoo: ClaRAN - A Deep Learning Classifier for Radio Morphologies
• Radio Galaxy Zoo: Compact and extended radio source classification with deep learning
• Unsupervised self-organised mapping: a versatile empirical tool for object selection, classification and redshift estimation in large surveys
• Extragalactic Radio Continuum Surveys and the Transformation of Radio Astronomy
• Radio Galaxy Zoo: Machine learning for radio source host galaxy cross-identification
• Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps

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• DeepGalaxy: Deducing the Properties of Galaxy Mergers from Images Using Deep Neural Networks
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