The discrimination between star-forming and AGN galaxies in the absence
  of H{\alpha} and [NII]: A machine learning approach
  In the absence of the two emission lines H$\alpha$ and [NII] (6584\AA) in a
BPT diagram, we show that other spectral information is sufficiently
informative to distinguish AGN galaxies from star-forming galaxies. We use
pattern recognition methods and a sample of galaxy spectra from the Sloan
Digital Sky Survey (SDSS) to show that, in this survey, the flux and equivalent
width of [OIII] (5007\AA) and H$\beta$, along with the 4000\AA-break, can be
used to classify galaxies in a BPT diagram. This method provides a higher
accuracy of predictions than those which use stellar mass and [OIII]/H$\beta$.
First, we use BPT diagrams and various physical parameters to re-classify the
galaxies. Next, using confusion matrices, we determine the `correctly'
predicted classes as well as confused cases. In this way, we investigate the
effect of each parameter in the confusion matrices and rank the physical
parameters used in the discrimination of the different classes. We show that in
this survey, for example, $\rm{g - r}$ colour can provide the same accuracy as
galaxy stellar mass to predict whether or not a galaxy hosts an AGN. Finally,
with the same information, we also rank the parameters involved in the
discrimination of Seyfert and LINER galaxies.
