期刊
ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
卷 230, 期 2, 页码 -出版社
IOP Publishing Ltd
DOI: 10.3847/1538-4365/aa7333
关键词
methods: miscellaneous; methods: observational; radio continuum: galaxies; techniques: miscellaneous
资金
- Square Kilometer Array South African Project (SKA SA)
- SKA SA postgraduate bursary program
- South African Research Chair Initiative (SARChI) program
We present the application of a. deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the. 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 survey and existing visually classified samples available in the 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 similar to 200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs 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 a. 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 the 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 the. morphological classification of radio galaxies with CNNs.
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