Increasing AGN sample completeness using long-term near-infrared variability

TL;DR

This study demonstrates that long-term near-infrared variability is an effective method for identifying a broader and more diverse sample of active galactic nuclei (AGN) than traditional X-ray detection, especially in lower-mass galaxies.

Contribution

The paper introduces a new approach using 8 years of NIR variability to identify AGN, expanding detection to lower-mass host galaxies and complementing X-ray surveys.

Findings

NIR variability detects AGN in a wider range of host galaxy masses.

Only 37% overlap between NIR-variable and X-ray detected AGN.

NIR variability identifies X-ray quiet AGN.

Abstract

In this work, we use 8 years of deep near-infrared imaging to select and study a new set of 601 active galaxies identified through long-term near-infrared (NIR) variability in the UKIDSS Ultra Deep Survey (UDS). These objects are compared to 710 X-ray bright AGN detected by the Chandra X-ray observatory. We show that infrared variability and X-ray emission select distinct sets of active galaxies, finding only a 37 per cent overlap of galaxies detected by both techniques and confirming NIR-variable AGN to be typically X-ray quiet. Examining the mass functions of the active galaxies shows that NIR variability detects AGN activity in galaxies over a significantly wider range of host stellar mass compared to X-ray detection. For example, at z $\sim$ 1, variable AGN are identified among approximately 1 per cent of galaxies in a roughly flat distribution above the stellar mass completeness limit (> 10$^{9}$M$_{\odot}$), while X-ray detection primarily identifies AGN in galaxies of higher mass (> 10$^{10}$M$_{\odot}$). We conclude that long-term near-infrared variability provides an important new tool for obtaining more complete samples of AGN in deep survey fields.