Emission Line-Independent Method to Classify Seyfert Galaxies

This study aims to classify Seyfert Type 1 and Seyfert Type 2 galaxies by differences other than the ratio of the strengths of their emission lines, which may allow researchers to discover something new about them that could not be discovered from spectral lines alone. We made use of 43,029 Seyferts from SIMBAD and used Python to analyze 5 properties of each galaxy: spatial distribution, redshift, color-magnitude, morphology, and luminosity. Analysis was done via inspection of graphs and descriptive statistics. Significant differences were found in luminosity, redshift, and color-magnitude. Based on these differences, we trained Decision Tree and Random Forest models to classify a given set of Seyferts as Sy1 and Sy2. The classification was accurate for 76.5% and 78.8% of the testing set for the respective models. Based on our f indings, it can be concluded that our model could provide information about Seyfert properties independent of their emission lines.