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

## TL;DR

This paper presents a new catalog of 275 AGN candidates from the AKARI NEP-Deep field, using a fuzzy SVM algorithm that incorporates measurement uncertainties for improved classification accuracy.

## Contribution

The study introduces a fuzzy SVM approach for AGN selection that effectively utilizes infrared data and measurement uncertainties, creating a reliable candidate catalog.

## Key findings

- Identified 275 new AGN candidates in the AKARI NEP-Deep field.
- Demonstrated the effectiveness of fuzzy SVM in astrophysical object classification.
- Produced a high-quality, unclassified AGN candidate catalog.

## 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.