Searching for Changing-state AGNs in Massive Data Sets. I. Applying Deep Learning and Anomaly-detection Techniques to Find AGNs with Anomalous Variability Behaviors

  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},
  author={P. S'anchez-S'aez and Hernan Lira and Luis Mart'i and N. S'anchez-Pi and J. Arredondo and Franz Erik Bauer and Amelia Bayo and Guillermo Cabrera-Vives and C. Donoso-Oliva and Pablo A. Est'evez and Susana Eyheramendy and Francisco F{\'o}rster and L. Hernandez-Garcia and Alejandra M. Mu{\~n}oz Arancibia and Manuel P'erez-Carrasco and Jorge R. Vergara},
  journal={The Astronomical Journal},
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… 

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1Department of Astronomy, Faculty of Mathematics, University of Belgrade, Studentski trg 16, 11000 Belgrade, Serbia 2Fellow of Chinese Academy of Sciences President’s International Fellowship

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