toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
  Record Links
Author (up) Sanchez-Saez, P.; Lira, H.; Marti, L.; Sanchez-Pi, N.; Arredondo, J.; Bauer, F.E.; Bayo, A.; Cabrera-Vives, G.; Donoso-Oliva, C.; Estevez, P.A.; Eyheramendy, S.; Forster, F.; Hernandez-Garcia, L.; Arancibia, A.M.M.; Perez-Carrasco, M.; Sepulveda, M.; Vergara, J.R. doi  openurl
  Title Searching for Changing-state AGNs in Massive Data Sets. I. Applying Deep Learning and Anomaly-detection Techniques to Find AGNs with Anomalous Variability Behaviors Type
  Year 2021 Publication Astronomical Journal Abbreviated Journal Astron. J.  
  Volume 162 Issue 5 Pages 206  
  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.  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0004-6256 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000709102700001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1483  
Permanent link to this record
Select All    Deselect All
 |   | 

Save Citations:
Export Records: