Deep Recurrent Neural Networks for Supernovae Classification

@article{Charnock2016DeepRN,
  title={Deep Recurrent Neural Networks for Supernovae Classification},
  author={Tom Charnock and Adam Moss},
  journal={ArXiv},
  year={2016},
  volume={abs/1606.07442}
}
  • Tom Charnock, Adam Moss
  • Published 2016
  • Computer Science, Physics
  • ArXiv
  • We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae (code available at https://github.com/adammoss/supernovae). The observational time and filter fluxes are used as inputs to the network, but since the inputs are agnostic, additional data such as host galaxy information can also be included. Using the Supernovae Photometric Classification Challenge (SPCC) data, we find that deep networks are capable of learning about… CONTINUE READING

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