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Register a new userAGN selection in the AKARI NEP deep field with the fuzzy SVM algorithm
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The aim of this work is to create a new catalog of reliable AGN candidates selected from the AKARI NEP-Deep field. Selection of the AGN candidates was done by applying a fuzzy SVM algorithm, which allows to incorporate measurement uncertainties into the classification process. The training dataset was based on the spectroscopic data available for selected objects in the NEP-Deep and NEP-Wide fields. The generalization sample was based on the AKARI NEP-Deep field data including objects without optical counterparts and making use of the infrared information only. A high quality catalog of previously unclassified 275 AGN candidates was prepared.
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Context: It is crucial to develop a method for classifying objects detected in deep surveys at infrared wavelengths. We specifically need a method to separate galaxies from stars using only the infrared information to study the properties of galaxies, e.g., to estimate the angular correlation function, without introducing any additional bias. Aims. We aim to separate stars and galaxies in the data from the AKARI North Ecliptic Pole (NEP) Deep survey collected in nine AKARI / IRC bands from 2 to 24 {mu}m that cover the near- and mid-infrared wavelengths (hereafter NIR and MIR). We plan to estimate the correlation function for NIR and MIR galaxies from a sample selected according to our criteria in future research. Methods: We used support vector machines (SVM) to study the distribution of stars and galaxies in the AKARIs multicolor space. We defined the training samples of these objects by calculating their infrared stellarity parameter (sgc). We created the most efficient classifier and then tested it on the whole sample. We confirmed the developed separation with auxiliary optical data obtained by the Subaru telescope and by creating Euclidean normalized number count plots. Results: We obtain a 90% accuracy in pinpointing galaxies and 98% accuracy for stars in infrared multicolor space with the infrared SVM classifier. The source counts and comparison with the optical data (with a consistency of 65% for selecting stars and 96% for galaxies) confirm that our star/galaxy separation methods are reliable. Conclusions: The infrared classifier derived with the SVM method based on infrared sgc- selected training samples proves to be very efficient and accurate in selecting stars and galaxies in deep surveys at infrared wavelengths carried out without any previous target object selection.
In this research, we provide a new, efficient method to select infrared (IR) active galatic nucleus (AGN). In the past, AGN selection in IR had been established by many studies using color-color diagrams. However, those methods have a problem in common that the number of bands is limited. The AKARI North Ecliptic Pole (NEP) survey was carried out by the AKARI Infrared Camera (IRC), which has 9 filters in mid-IR with a continuous wavelength coverage from 2 to 24$mu$m$^{-1}$. Based on the intrinsic different mid-IR features of AGN and star-forming galaxies (SFGs), we performed SED fitting to separate these two populations by the best-fitting model. In the X-ray AGN sample, our method by SED fitting selects 50$%$ AGNs, while the previous method by colour criteria recovers only 30$%$ of them, which is a significant improvement. Furthermore, in the whole NEP deep sample, SED fitting selects two times more AGNs than the color selection. This may imply that the black hole accretion history could be more stronger than people expected before.
We have developed an efficient Active Galactic Nucleus (AGN) selection method using 18-band Spectral Energy Distribution (SED) fitting in mid-infrared (mid-IR). AGNs are often obscured by gas and dust, and those obscured AGNs tend to be missed in optical, UV and soft X-ray observations. Mid-IR light can help us to recover them in an obscuration free way using their thermal emission. On the other hand, Star-Forming Galaxies (SFG) also have strong PAH emission features in mid-IR. Hence, establishing an accurate method to separate populations of AGN and SFG is important. However, in previous mid-IR surveys, only 3 or 4 filters were available, and thus the selection was limited. We combined AKARIs continuous 9 mid-IR bands with WISE and Spitzer data to create 18 mid-IR bands for AGN selection. Among 4682 galaxies in the AKARI NEP deep field, 1388 are selected to be AGN hosts, which implies an AGN fraction of 29.6$pm$0.8$%$ (among them 47$%$ are Seyfert 1.8 and 2). Comparing the result from SED fitting into WISE and Spitzer colour-colour diagram reveals that Seyferts are often missed by previous studies. Our result has been tested by stacking median magnitude for each sample. Using X-ray data from Chandra, we compared the result of our SED fitting with WISEs colour box selection. We recovered more X-ray detected AGN than previous methods by 20$%$.
We present a preliminary analysis of clustering of galaxies luminous in the near- and mid-infrared as seen by seven various ilters of the AKARI IRC instrument from 2 $mu$m to 24 $mu$m in the the AKARI NEP-Deep field. We compare populations of galaxies detected in different filters and their clustering properties. We conclude that different AKARI filters allow to trace different populations composed mainly of star-forming galaxies located in different environments. In particular, the mid-infrared filters at redshift z $sim$ 0.8 and higher trace a population of strongly evolving galaxies located in massive haloes which might have ended as elliptical galaxies today.
Radio-loud active galaxies have been found to exhibit a close connection to galactic mergers and host galaxy star-formation quenching. We present preliminary results of an optical spectroscopic investigation of the AKARI NEP field. We focus on the population of radio-loud AGN and use photometric and spectroscopic information to study both their star-formation and nuclear activity components. Preliminary results show that radio-AGN are associated with early type, massive galaxies with relatively old stellar populations.
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