J/ApJS/256/34 SDSS Seyfert 1.9 galaxies with Ha emission line fit (Chen, 2021)
Classifying Seyfert galaxies with deep learning.
Chen Y.C.
<Astrophys. J. Suppl. Ser., 256, 34-34 (2021)>
=2021ApJS..256...34C 2021ApJS..256...34C (SIMBAD/NED BibCode)
ADC_Keywords: Active gal. nuclei; Galaxies, Seyfert; Spectra, optical; Redshifts
Keywords: Active galaxies; Seyfert galaxies; Observational astronomy
Galaxy spectroscopy; Catalogs
Abstract:
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.
Description:
We select our Seyfert sources from Chen & Hwang (2019MNRAS.485.3402C 2019MNRAS.485.3402C).
This sample includes 54694 Seyfert 2 galaxies and 745 Seyfert 1.9
galaxies with 0<z<0.2. Our sample criteria are a signal-to-noise ratio
(S/N) of Hα>3, an S/N of [OIII]>5, and log L[OIII] [erg/s]>40.125.
We obtain the optical spectra from the SDSS Data Release 10.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table4.dat 90 1297 Catalog of the Seyfert 1.9 galaxies with
Hα emission line measurement
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See also:
VII/258 : Quasars and Active Galactic Nuclei (13th Ed.) (Veron+ 2010)
V/154 : Sloan Digital Sky Surveys (SDSS), Release 16 (DR16) (Ahumada+, 2020)
J/ApJ/679/86 : AGN-host connection in partially obscured AGNs. I. (Wang+, 2008)
J/MNRAS/423/600 : Type-1 low-z AGN emission properties (Stern+, 2012)
J/MNRAS/437/968 : AGN automatic photometric classification (Cavuoti+, 2014)
J/ApJ/794/120 : Sloan Digital Sky Survey coadd. Stripe 82 (Annis+, 2014)
J/A+A/611/A97 : Photometric quasar cand. in Stripe 82 (Pasquet-Itam+, 2018)
J/MNRAS/492/468 : Chemical abundances of Seyfert 2 AGNs - I (Dors+, 2020)
J/A+A/649/A53 : Mean galaxy spectra of the 86 classes (Fraix-Burnet+, 2021)
Byte-by-byte Description of file: table4.dat
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Bytes Format Units Label Explanations
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1- 9 F9.5 deg RAdeg Right Ascension (J2000)
11- 19 F9.5 deg DEdeg [-11/68.6] Declination (J2000)
21- 26 F6.4 --- z [0.02/0.2] Spectroscopic redshift
28- 31 I4 --- Plate [266/2974] SDSS spectroscopic plate number
33- 37 I5 d MJD [51614/54592] Modified Julian Date of SDSS
observation
39- 41 I3 --- Fiber [1/640] SDSS FiberID number
43- 50 E8.2 10-7W LHaB [1e+40/9.7e+42] Broad Hα component
luminosity (1)
52- 58 E7.1 10-7W e_LHaB [2.9e+38/4.7e+42] Uncertainty in LHaB (1)
60- 67 E8.2 10-7W LHaN [1.7e+39/3.2e+42] Narrow Hα component
luminosity (1)
69- 75 E7.1 10-7W e_LHaN [3e+38/1.6e+43] Uncertainty in LHaN (1)
77- 83 F7.1 km/s FWHM [328/10780] Broad Hα component FWHM
85- 90 F6.1 km/s e_FWHM [2.8/2963] Uncertainty in FWHM
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Note (1): In units of erg/s.
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History:
From electronic version of the journal
(End) Prepared by [AAS], Emmanuelle Perret [CDS] 12-Feb-2024