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