2,854 research outputs found
A Novel Method to Identify AGNs Based on Emission Line Excess and the Nature of Low-luminosity AGNs in the Sloan Digital Sky Survey: I - A Novel Method
(Abridged) We develop a novel technique to identify active galactic nuclei
(AGNs) and study the nature of low-luminosity AGNs in the Sloan Digital Sky
Survey. This is the first part of a series of papers and we develop a new,
sensitive method to identify AGNs in this paper. An emission line luminosity in
a spectrum is a sum of a star formation component and an AGN component (if
present). We demonstrate that an accurate estimate of the star formation
component can be achieved by fitting model spectra, generated with a recent
stellar population synthesis code, to a continuum spectrum. By comparing the
observed total line luminosity with that attributed to star formation, we can
tell whether a galaxy host an AGN or not. We compare our method with the
commonly used emission line diagnostics proposed by Baldwin et al. (1981;
hereafter BPT). Our method recovers the same star formation/AGN classification
as BPT for 85% of the strong emission line objects, which comprise 43% of our
sample. A unique feature of our method is its sensitivity: it is applicable to
78% of the sample. We further make comparisons between our method and BPT using
stacked spectra and selection in X-ray and radio wavelengths. We show that,
while the method suffers from incompleteness and contamination as any AGN
identification methods do, it is overall a sensitive method to identify AGNs.
Another unique feature of the method is that it allows us to subtract emission
line luminosity due to star formation and extract intrinsic AGN luminosity. We
will make a full use of these features to study the nature of low-luminosity
AGNs in Paper-II.Comment: 21 pages, 22 figures, PASJ in press. Minor change
A Novel Method to Identify AGNs Based on Emission Line Excess and the Nature of Low-luminosity AGNs in the Sloan Digital Sky Survey: II - Nature of Low-luminosity AGNs
We develop a novel method to identify active galactic nuclei (AGNs) and study
the nature of low-luminosity AGNs in the Sloan Digital Sky Survey. This is the
second part of a series of papers and we study the correlations between the AGN
activities and host galaxy properties. Based on a sample of AGNs identified
with the new method developed in Paper-I, we find that AGNs typically show
extinction of tau_V=1.2 and they exhibit a wide range of ionization levels. The
latter finding motivates us to use [OII]+[OIII] luminosity as an indicator of
AGN power. We find that AGNs are preferentially located in massive, red,
early-type galaxies. By carefully taking into account a selection bias of the
Oxygen-excess method, we show that strong AGNs are located in actively star
forming galaxies and rapidly growing super-massive black holes are located in
rapidly growing galaxies, which clearly shows the co-evolution of super-massive
black holes and the host galaxies. This is a surprising phenomenon given that
the growths of black holes and host galaxies occur at very different physical
scales. Interestingly, the AGN power does not strongly correlate with the host
galaxy mass. It seems that mass works like a 'switch' to activate AGNs. The
absence of AGNs in low-mass galaxies might be due the absence of super-massive
black holes in those galaxies, but a dedicated observation of nuclear region of
nearby low-mass galaxies would be necessarily to obtain deeper insights into
it.Comment: 19 pages, 19 figures, PASJ in press. Minor change
Non-blind Image Restoration Based on Convolutional Neural Network
Blind image restoration processors based on convolutional neural network
(CNN) are intensively researched because of their high performance. However,
they are too sensitive to the perturbation of the degradation model. They
easily fail to restore the image whose degradation model is slightly different
from the trained degradation model. In this paper, we propose a non-blind
CNN-based image restoration processor, aiming to be robust against a
perturbation of the degradation model compared to the blind restoration
processor. Experimental comparisons demonstrate that the proposed non-blind
CNN-based image restoration processor can robustly restore images compared to
existing blind CNN-based image restoration processors.Comment: Accepted by IEEE 7th Global Conference on Consumer Electronics, 201
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