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Machine learning techniques have been increasingly useful in astronomicalapplications over the last few years, for example in the morphologicalclassification of galaxies. Convolutional neural networks have proven to behighly effective in classifying objects in image data. The current work aims toestablish when multiple components are present, in the astronomical context ofsynthesis imaging observations of radio sources. To this effect, we design aconvolutional neural network to differentiate between different morphologyclasses using sources from the Radio Galaxy Zoo (RGZ) citizen science project.In this first step, we focus on exploring the factors that affect theperformance of such neural networks, such as the amount of training data,number and nature of layers and the hyperparameters. We begin with a simpleexperiment in which we only differentiate between two extreme morphologies,using compact and multiple component extended sources. We found that a threeconvolutional layer architecture yielded very good results, achieving aclassification accuracy of 97.4% on a test data set. The same architecture wasthen tested on a four-class problem where we let the network classify sourcesinto compact and three classes of extended sources, achieving a test achievinga test accuracy of 93.5%. The best-performing convolutional neural networksetup has been verified against RGZ Data Release 1 where a final test accuracyof 94.8% was obtained, using both original and augmented images. The use ofsigma clipping does not offer a significant benefit overall, except in caseswith a small number of training images.

Radio Galaxy Zoo: Compact and extended radio source classification with deep learning