Deep recurrent neural networks for supernovae classification
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, add...
| Main Authors: | , |
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| Format: | Article |
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American Astronomical Society
2017
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| Online Access: | https://eprints.nottingham.ac.uk/42324/ |