Selecting AGN through variability in SN datasets

TL;DR

This paper presents a method to select active galactic nuclei (AGN) based on their variability in supernova survey datasets, successfully identifying hundreds of candidates and confirming their AGN nature spectroscopically.

Contribution

The study introduces a variability-based AGN selection technique applied to SN datasets, expanding the identification of AGN candidates in large sky surveys.

Findings

Identified 132 AGN candidates in the AXAF field.

Extended the method to the ESSENCE dataset, finding ~4800 variable sources.

Spectroscopic confirmation of nearly all high-priority candidates.

Abstract

Variability is a main property of active galactic nuclei (AGN) and it was adopted as a selection criterion using multi epoch surveys conducted for the detection of supernovae (SNe). We have used two SN datasets. First we selected the AXAF field of the STRESS project, centered in the Chandra Deep Field South where, besides the deep X-ray surveys also various optical catalogs exist. Our method yielded 132 variable AGN candidates. We then extended our method including the dataset of the ESSENCE project that has been active for 6 years, producing high quality light curves in the R and I bands. We obtained a sample of ~4800 variable sources, down to R=22, in the whole 12 deg^2 ESSENCE field. Among them, a subsample of ~500 high priority AGN candidates was created using as secondary criterion the shape of the structure function. In a pilot spectroscopic run we have confirmed the AGN nature for nearly all of our candidates.