Skip to Main content Skip to Navigation
Journal articles

Searching for changing-state AGNs in massive datasets -- I: applying deep learning and anomaly detection techniques to find AGNs with anomalous variability behaviours

Abstract : The classic classification scheme for active galactic nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs. The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods that are able to detect AGNs right in the act of changing state. Here we present an anomaly-detection technique designed to identify AGN light curves with anomalous behaviors in massive data sets. The main aim of this technique is to identify CSAGN at different stages of the transition, but it can also be used for more general purposes, such as cleaning massive data sets for AGN variability analyses. We used light curves from the Zwicky Transient Facility data release 5 (ZTF DR5), containing a sample of 230,451 AGNs of different classes. The ZTF DR5 light curves were modeled with a Variational Recurrent Autoencoder (VRAE) architecture, that allowed us to obtain a set of attributes from the VRAE latent space that describes the general behavior of our sample. These attributes were then used as features for an Isolation Forest (IF) algorithm that is an anomaly detector for a "one class" kind of problem. We used the VRAE reconstruction errors and the IF anomaly score to select a sample of 8809 anomalies. These anomalies are dominated by bogus candidates, but we were able to identify 75 promising CSAGN candidates.
Complete list of metadata

https://hal.inria.fr/hal-03294849
Contributor : Luis Martí Connect in order to contact the contributor
Submitted on : Wednesday, October 20, 2021 - 8:33:14 PM
Last modification on : Friday, February 4, 2022 - 3:09:41 AM
Long-term archiving on: : Friday, January 21, 2022 - 8:38:15 PM

File

aj_162_5_206.pdf
Publisher files allowed on an open archive

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Collections

Citation

P. Sánchez-Sáez, Hernan Lira, Luis Martí, Nayat Sanchez-Pi, J. Arredondo, et al.. Searching for changing-state AGNs in massive datasets -- I: applying deep learning and anomaly detection techniques to find AGNs with anomalous variability behaviours. The Astronomical Journal, American Astronomical Society, 2021, 162 (5), ⟨10.3847/1538-3881/ac1426⟩. ⟨hal-03294849⟩

Share

Metrics

Record views

77

Files downloads

24