PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

@article{Lochner2016PHOTOMETRICSC,
  title={PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING},
  author={Michelle Lochner and Jason D. McEwen and Hiranya V. Peiris and Ofer Lahav and Max K. Winter},
  journal={The Astrophysical Journal Supplement Series},
  year={2016},
  volume={225}
}
Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the… 

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