The FIRST Classifier: Compact and Extended Radio Galaxy Classification
  using Deep Convolutional Neural Networks
  Upcoming surveys with new radio observatories such as the Square Kilometer
Array will generate a wealth of imaging data containing large numbers of radio
galaxies. Different classes of radio galaxies can be used as tracers of the
cosmic environment, including the dark matter density field, to address key
cosmological questions. Classifying these galaxies based on morphology is thus
an important step toward achieving the science goals of next generation radio
surveys. Radio galaxies have been traditionally been classified as
Fanaroff-Riley (FR) I and II, although some exhibit more complex 'bent'
morphologies arising from environmental factors or intrinsic properties. In
this work we present the FIRST Classifier, an on-line system for automated
classification of Compact and Extended radio sources. We developed the FIRST
Classifier based on a trained Deep Convolutional Neural Network Model to
automate the morphological classification of compact and extended radio sources
observed in the FIRST radio survey. Our model achieved an overall accuracy of
97% and a recall of 98%, 100%, 98% and 93% for Compact, BENT, FRI and FRII
galaxies respectively. The current version of the FIRST classifier is able to
predict the morphological class for a single source or for a list of sources as
Compact or Extended (FRI, FRII and BENT).
