Classifying Radio Galaxies with Convolutional Neural Network

Abstract: We present the application of deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks. In this study, we have taken the case of Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA) - Faint Images of the Radio Sky at Twenty Centimeters (FIRST) survey and existing visually classified samples available in literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is approximately 200 sources, which has been augmented by rotated versions of the same. Our study shows that convolutional neural networks can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and fusion classifier, which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification while being much faster. Finally, we discuss the computational and data-related challenges associated with morphological classification of radio galaxies with convolutional neural networks.