2021/05/13 by Viera Maslej-Krešňáková, Viera Krešňáková, Khadija El Bouchefry +1
Computer Science · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Astronomy #Astronomy and Astrophysical Research #Astrophysics #Brightness #Class (philosophy) #Computer science #Convolutional neural network #Galaxies: Formation, Evolution, Phenomena #Galaxy #Optics #Pattern recognition (psychology) #Physics #Radio Astronomy Observations and Technology #Radio galaxy #Zoom #astro-ph.GA #astro-ph.IM #cs.LG #cs.NE
paper · pdf · doi:10.1093/mnras/stab1400
published as Mon Not Roy Astron Soc 505 (2021) 1464-1475 · 12 pages, 7 figures, 9 tables, published in Monthly Notices of the Royal Astronomical Society
openalex publication_date 2021/05/13 · openalex created_date 2021/05/24 · arxiv created 2021/07/01 · arxiv updated 2021/07/02 · openalex updated_date 2026/08/05
ABSTRACT 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 (CNNs). The proposed architecture comprises three parallel blocks of convolutional layers combined and processed for final classification by two feed-forward layers. Our model classified selected classes of radio galaxy sources on an independent testing subset with an average of 96 per cent for precision, recall, and F1 score. The best selected augmentation techniques were rotations, horizontal or vertical flips, and increase of brightness. Shifts, zoom, and decrease of brightness worsened the performance of the model. The current results show that model developed in this work is able to identify different morphological classes of radio galaxies with a high efficiency and performance.