  We present an automated method for the detection of bar structure in optical
images of galaxies using a deep convolutional neural network which is easy to
use and provides good accuracy. In our study we use a sample of 9346 galaxies
in the redshift range 0.009-0.2 from the Sloan Digital Sky Survey, which has
3864 barred galaxies, the rest being unbarred. We reach a top precision of ~94
per cent in identifying bars in galaxies using the trained network. This
accuracy matches the accuracy reached by human experts on the same data without
additional information about the images. Since Deep Convolutional Neural
Networks can be scaled to handle large volumes of data, the method is expected
to have great relevance in an era where astronomy data is rapidly increasing in
terms of volume, variety, volatility and velocity along with other V's that
characterize big data. With the trained model we have constructed a catalogue
of barred galaxies from SDSS and made it available online.
