Morphological classification of compact and extended radio galaxies using convolutional neural networks and data augmentation techniques

  title={Morphological classification of compact and extended radio galaxies using convolutional neural networks and data augmentation techniques},
  author={Viera Maslej-Kre{\vs}ň{\'a}kov{\'a} and Khadija El Bouchefry and Peter Butka},
Machine-learning techniques have been increasingly used in astronomical applications and have proven to successfully classify objects in image data with high accuracy. The current work uses archival data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) to classify radio galaxies into four classes: Fanaroff–Riley Class I (FRI), Fanaroff–Riley Class II (FRII), Bent-Tailed (BENT), and Compact (COMPT). The model presented in this work is based on Convolutional Neural Networks… 
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