Classifying Seyfert galaxies with deep learning

TL;DR

This paper develops a 1D CNN model to classify Seyfert galaxy subclasses, achieving over 80% accuracy and identifying additional Seyfert 1.9 spectra missed by visual inspection, with implications for galaxy morphology and emission line analysis.

Contribution

The study introduces a deep learning approach using 1D CNNs to classify Seyfert galaxy subclasses, improving accuracy and discovering new Seyfert 1.9 spectra beyond traditional methods.

Findings

CNN achieved over 80% accuracy in classifying Seyfert 1.9 and Seyfert 2 spectra.

The model identified additional Seyfert 1.9 spectra missed by visual inspection.

The new Seyfert 1.9 sample has broader velocity distribution and slightly weaker luminosity.

Abstract

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.