Corpus ID: 235436127

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

@inproceedings{SanchezSaez2021SearchingFC,
  title={Searching for changing-state AGNs in massive datasets -- I: applying deep learning and anomaly detection techniques to find AGNs with anomalous variability behaviours},
  author={P. S'anchez-S'aez and H. Lira and L. Mart'i and N. S'anchez-Pi and J. Arredondo and F. Bauer and A. Bayo and G. Cabrera-Vives and C. Donoso-Oliva and P. Est'evez and S. Eyheramendy and F. F{\'o}rster and L. Hernandez-Garcia and A. M. Arancibia and M. P'erez-Carrasco and J. Vergara},
  year={2021}
}
The classic classification scheme for Active Galactic Nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs (CSAGNs). 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 AGN right in the act of changing–state. Here we present an anomaly detection (AD… Expand

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