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Context. The North Ecliptic Pole (NEP) field provides a unique set ofpanchromatic 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 dueto the MIR detection condition. Modern machine learning techniques can solvethis problem by finding similar selection criteria using only optical andnear-infrared (NIR) data. Aims. Aims of this work were to create a reliable AGNcandidates catalog from the NEP field using a combination of optical SUBARU/HSCand NIR AKARI/IRC data and, consequently, to develop an efficient alternativefor the MIR-based AKARI/IRC selection technique. Methods. A set of supervisedmachine learning algorithms was tested in order to perform an efficient AGNselection. Best of the models were formed into a majority voting scheme, whichused the most popular classification result to produce the final AGN catalog.Additional analysis of catalog properties was performed in form of the spectralenergy distribution (SED) fitting via the CIGALE software. Results. Theobtained catalog of 465 AGN candidates (out of 33 119 objects) is characterizedby 73% purity and 64% completeness. This new classification shows consistencywith the MIR-based selection. Moreover, 76% of the obtained catalog can befound only with the new method due to the lack of MIR detection for most of thenew AGN candidates. Training data, codes and final catalog are available viathe github repository. Final AGN candidates catalog will be also available viathe CDS service after publication.

Active galactic nuclei catalog from the AKARI NEP Wide field