Classifying Seyfert Galaxies with Deep Learning

Journal Article · Tue Sep 28 00:00:00 EDT 2021 · The Astrophysical Journal. Supplement Series

DOI:https://doi.org/10.3847/1538-4365/ac13aa· OSTI ID:1983298

Chen, Yen Chen ^[1]

---

1. Sapienza University of Rome (Italy); International Center for Relativistic Astrophysics Network (ICRANet) (Italy)

The traditional classification for a 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 the convolution neural network (CNN), which has shown successful classification results. We build a one-dimensional CNN model to distinguish Seyfert 1.9 spectra from Seyfert 2 galaxies. We find that our model can recognize Seyfert 1.9 and Seyfert 2 spectra with an accuracy of over 80% and pick out an additional Seyfert 1.9 sample that was missed by visual inspection. We use the new Seyfert 1.9 sample to improve the performance of our model and obtain a 91% precision of Seyfert 1.9. These results indicate that our model can pick out Seyfert 1.9 spectra among Seyfert 2 spectra. We decompose the Hα emission line of our Seyfert 1.9 galaxies by fitting two Gaussian components and derive the line width and flux. We find that the velocity distribution of the broad Hα component of the new Seyfert 1.9 sample has an extending tail toward the higher end, and the 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 a relatively weak broad Hα component. In addition, we check the distributions of the host galaxy morphology of our Seyfert 1.9 samples and find that the distribution of the host galaxy morphology is dominated by a large bulge galaxy. In the end, we present an online catalog of 1297 Seyfert 1.9 galaxies with measurements of the Hα emission line.

• Research Organization: US Department of Energy (USDOE), Washington, DC (United States). Office of Science, Sloan Digital Sky Survey (SDSS)
• Sponsoring Organization: Alfred P. Sloan Foundation; National Science Foundation (NSF); USDOE Office of Science (SC)
• Contributing Organization: Brookhaven National Laboratory (BNL); Lawrence Berkeley National Laboratory (LBNL)
• OSTI ID: 1983298
• Alternate ID(s): OSTI ID: 23163620
• Journal Information: The Astrophysical Journal. Supplement Series, Journal Name: The Astrophysical Journal. Supplement Series Journal Issue: 2 Vol. 256; ISSN 0067-0049
• Publisher: IOP Publishing
• Country of Publication: United States
• Language: English

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
Active galaxies
Catalogs
Galaxy spectroscopy
Observational astronomy
Seyfert galaxies