AGN selection in the AKARI NEP deep field with the fuzzy SVM algorithm

Abstract

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.

17 pages, accepted to PASJ AKARI special issue

References in corpus (6)

• The WISE AGN Catalog
• Machine-learning identification of galaxies in the WISExSuperCOSMOS all-sky catalogue
• Chandra survey in the AKARI North Ecliptic Pole Deep Field. I. X-ray data, point-like source catalog, sensitivity maps, and number counts
• Towards automatic classification of all WISE sources
• Automated novelty detection in the WISE survey with one-class support vector machines
• An extinction free AGN selection by 18-band SED fitting in mid-infrared in the AKARI NEP deep field

Cited by in corpus (4)

• Search for Optically Dark Infrared Galaxies without Counterparts of Subaru Hyper Suprime-Cam in the AKARI North Ecliptic Pole Wide Survey Field
• An Active Galactic Nucleus Recognition Model based on Deep Neural Network
• Optically-detected galaxy cluster candidates in the $AKARI$ North Ecliptic Pole field based on photometric redshift from Subaru Hyper Suprime-Cam
• Active galactic nuclei catalog from the AKARI NEP Wide field