AGN catalog from the AKARI NEP Wide field


The North Ecliptic Pole (NEP) field provides a unique set of panchromatic data, well suited for active galactic nuclei (AGN) studies. Selection of AGN candidates is often based on mid-infrared (MIR) measurements. Such method, despite its effectiveness, strongly reduces a catalog volume due to the MIR detection condition. Modern machine learning techniques can solve this problem by finding similar selection criteria using only optical and near-infrared (NIR) data. Aims of this work were to create a reliable AGN candidates catalog from the NEP field using a combination of optical SUBARU/HSC and NIR AKARI/IRC data and, consequently, to develop an efficient alternative for the MIR-based AKARI/IRC selection technique. A set of supervised machine learning algorithms was tested in order to perform an efficient AGN selection. Best of the models were formed into a majority voting scheme, which used the most popular classification result to produce the final AGN catalog. Additional analysis of catalog properties was performed in form of the spectral energy distribution (SED) fitting via the CIGALE software. The obtained catalog of 465 AGN candidates (out of 33119 objects) is characterized by 73% purity and 64% completeness. This new classification shows consistency with the MIR-based selection. Moreover, 76% of the obtained catalog can be found only with the new method due to the lack of MIR detection for most of the new AGN candidates. Training data, codes and final catalog are available via the github repository. Final AGN candidates catalog is also available via the CDS service.

Cone search capability for table J/A+A/651/A108/catalog (Catalog of AGN candidates (465 objects))

Related Identifier
Related Identifier
Metadata Access
Creator Poliszczuk A.; Pollo A.; Malk K.; Durkalec A.; Pearson W.J.; Goto T.; Kim S.-J.; Malkan M.; Oi N.; Ho S.C.-C.; Shim H.; Pearson C.; Hwang H.-S.; Toba Y.; Kim E.
Publisher CDS
Publication Year 2021
OpenAccess true
Contact CDS support team <cds-question(at)>
Resource Type Dataset
Discipline Astrophysics and Astronomy; Galactic and extragalactic Astronomy; High Energy Astrophysics; Natural Sciences; Observational Astronomy; Physics