Navigating AGN variability with self-organizing maps

TL;DR

This paper explores using self-organizing maps to classify active galactic nuclei based on light curve variability features, aiming to improve AGN identification in upcoming large-scale surveys like LSST.

Contribution

It demonstrates the effectiveness of SOMs in classifying AGNs using variability features and analyzes how different feature subsets affect classification purity and completeness.

Findings

SOMs produce a relatively pure AGN sample.

Type 2 AGNs are the hardest to identify.

Using variability features aids AGN classification.

Abstract

Context. The classification of active galactic nuclei (AGNs) is a challenge in astrophysics. Variability features extracted from light curves offer a promising avenue for distinguishing AGNs and their subclasses. This approach would be very valuable in sight of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Aims. Our goal is to utilize self-organizing maps (SOMs) to classify AGNs based on variability features and investigate how the use of different subsets of features impacts the purity and completeness of the resulting classifications. Methods. We derived a set of variability features from light curves, similar to those employed in previous studies, and applied SOMs to explore the distribution of AGNs subclasses. We conducted a comparative analysis of the classifications obtained with different subsets of features, focusing on the ability to identify different AGNs types. Results. Our analysis demonstrates that using SOMs with variability features yields a relatively pure AGNs sample, though completeness remains a challenge. In particular, Type 2 AGNs are the hardest to identify, as can be expected. These results represent a promising step toward the development of tools that may support AGNs selection in future large-scale surveys such as LSST.