Navigating AGN variability with self-organizing maps

¹ INAF - Astronomical Observatory of Capodimonte, Via Moiariello 16, 80131 Napoli, Italy
² Department of Physics “E. Pancini”, University Federico II of Napoli, Via Cinthia 21, 80126 Napoli, Italy
³ Millennium Institute of Astrophysics (MAS), Nuncio Monseñor Sotero Sanz 100, Providencia, Santiago, Chile
⁴ INFN section of Naples, via Cinthia 6, 80126 Napoli, Italy
⁵ European Southern Observatory, Karl-Schwarzschild-Strasse 2, 85748 Garching bei München, Germany
⁶ Intesa Sanpaolo S.p.A., Corso Inghilterra 3, 10138 Turin, Italy

^★ Corresponding author

Received: 23 January 2025
Accepted: 12 June 2025

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.