Active galactic nuclei catalog from the AKARI NEP Wide field
Artem Poliszczuk, Agnieszka Pollo, Katarzyna Ma{\l}ek, Anna Durkalec,, William J. Pearson, Tomotsugu Goto, Seong Jin Kim, Matthew Malkan, Nagisa Oi,, Simon C.-C. Ho, Hyunjin Shim, Chris Pearson, Ho Seong Hwang, Yoshiki Toba and, Eunbin Kim

TL;DR
This study develops a machine learning-based method to identify active galactic nuclei (AGN) using optical and near-infrared data, creating a catalog that complements traditional mid-infrared selection techniques.
Contribution
The paper introduces a novel machine learning approach for AGN selection that does not rely on mid-infrared data, expanding the catalog of AGN candidates in the NEP field.
Findings
Catalog of 465 AGN candidates with 73% purity and 64% completeness.
Method identifies 76% of AGN candidates only detectable with optical/NIR data.
Results are consistent with traditional MIR-based AGN selection.
Abstract
Context. 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. 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. Methods. 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…
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