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تسجيل مستخدم جديدAGN 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
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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 comm
on 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.
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