Classifying Seyfert galaxies with deep learning

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 Hα 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 Hα 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 Hα 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 Hα emission line.

accepted for publication in ApJS

References in corpus (25)

• The Sloan Digital Sky Survey: Technical Summary
• The Host Galaxies of AGN
• Theoretical Modeling of Starburst Galaxies
• The Host Galaxies and Classification of Active Galactic Nuclei
• The Palomar Transient Factory: System Overview, Performance and First Results
• The Tenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Apache Point Observatory Galactic Evolution Experiment
• Observational evidence for AGN feedback in early-type galaxies
• Exploring the Optical Transient Sky with the Palomar Transient Factory
• A Search for "Dwarf" Seyfert Nuclei. IV. Nuclei with Broad H-alpha Emission
• A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning
• Deep learning of multi-element abundances from high-resolution spectroscopic data
• The SDSS Coadd: 275 deg^2 of Deep SDSS Imaging on Stripe 82
• Type 1 low z AGN. I. Emission properties
• Deep learning Approach for Classifying, Detecting and Predicting Photometric Redshifts of Quasars in the Sloan Digital Sky Survey Stripe 82
• A Multi-Wavelength Study of the Nature of Type 1.8/1.9 Seyfert Galaxies
• Photometric classification of emission line galaxies with Machine Learning methods
• Long-Term Variability in the Optical Spectrum of the Seyfert Galaxy NGC 2992
• Understanding AGN-Host Connection in Partially Obscured Active Galactic Nuclei. Part I: The Nature of AGN+HII Composites
• The QUEST-La Silla AGN Variability Survey: selection of AGN candidates through optical variability
• How to Find Variable Active Galactic Nuclei with Machine Learning
• Machine Learning Classifiers for Intermediate Redshift Emission Line Galaxies
• Unsupervised classification of SDSS galaxy spectra
• The discrimination between star-forming and AGN galaxies in the absence of Hα and [NII]: A machine learning approach
• Morphology of Seyfert Galaxies
• Emission Line Luminosity Distributions of Seyfert 2 Galaxies

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