Reducing the Dimensions of AGN Lightcurve Manifolds

TL;DR

This paper develops a data-driven, low-dimensional manifold representation of AGN light curves that correlates with physical properties and classifications, enabling better understanding of AGN diversity and variability.

Contribution

The study introduces a novel manifold learning approach to organize multi-wavelength AGN light curves without labels, linking variability to physical characteristics and classifications.

Findings

Manifold coordinates distinguish AGN classes and changing-look AGNs.

Variability-based manifolds correlate with spectroscopic properties.

Method organizes diverse AGN data into meaningful low-dimensional space.

Abstract

The Active Galactic Nuclei (AGN) glossary is vast and complex. Depending on selection method, observing wavelength, and brightness, AGNs are assigned distinct labels, yet the relationship between different selection methods and the diversity of time-domain behavior within and across classes remains difficult to characterize in a unified framework. Changing-look AGNs (CLAGNs), which transition between classifications over time, further complicate this picture. In this work, we learn a data-driven, low-dimensional representation of multi-wavelength photometric light curves of AGNs, in which the structure of the projected manifold correlates with AGN class and independent spectroscopic properties. Using the NASA Fornax Science Platform, we assemble light curves from ZTF, Pan-STARRS, Gaia, and WISE/NEOWISE for two samples: (1) a heterogeneous set of $\sim$2000 AGNs spanning $z \lesssim 1$, including SDSS quasars, variability-selected sources, and CLAGNs; and (2) a homogeneous sample of $\sim$65000 narrow-line AGNs at $z \approx 0.1$ with well-characterized optical emission-line measurements. Without using class labels during training, the learned manifolds organize variability-selected AGNs into coherent regions of the low-dimensional space, distinguish between turn-on and turn-off CLAGNs, and place tidal disruption events in distinct regions. Manifold coordinates correlate with key spectroscopic and host-galaxy properties, including stellar mass, [OIII] luminosity, and D$_n$(4000), demonstrating that heterogeneous multi-band variability can be combined in a purely data-driven manner to recover correlations with independent physical diagnostics, without requiring explicit physical modeling. These results show that manifold learning offers a practical, assumption-light approach for integrating time-domain surveys and prioritizing spectroscopic follow-up.