Classifying Seyfert galaxies with deep learning
  Traditional classification for subclass of the Seyfert galaxies is visual
inspection or using a quantity defined as a flux ratio between the Balmer line
and forbidden line. One algorithm of deep learning is Convolution Neural
Network (CNN) and has shown successful classification results. We building a
1-dimension CNN model to distinguish Seyfert 1.9 spectra from Seyfert 2
galaxies. We find our model can recognize Seyfert 1.9 and Seyfert 2 spectra
with an accuracy over 80% and pick out an additional Seyfert 1.9 sample which
was missed by visual inspection. We use the new Seyfert 1.9 sample to improve
performance of our model and obtain a 91% precision of Seyfert 1.9. These
results indicate our model can pick out Seyfert 1.9 spectra among Seyfert 2
spectra. We decompose H{\alpha} emission line of our Seyfert 1.9 galaxies by
fitting 2 Gaussian components and derive line width and flux. We find velocity
distribution of broad H{\alpha} component of the new Seyfert 1.9 sample has an
extending tail toward the higher end and luminosity of the new Seyfert 1.9
sample is slightly weaker than the original Seyfert 1.9 sample. This result
indicates that our model can pick out the sources that have relatively weak
broad H{\alpha} component. Besides, we check distributions of the host galaxy
morphology of our Seyfert 1.9 samples and find the distribution of the host
galaxy morphology is dominant by large bulge galaxy. In the end, we present an
online catalog of 1297 Seyfert 1.9 galaxies with measurement of H{\alpha}
emission line.
