Deep recurrent neural networks for supernovae classification

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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...

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Bibliographic Details
Main Authors:	Charnock, Tom, Moss, Adam
Format:	Article
Published:	American Astronomical Society 2017
Subjects:	methods: data analysis – supernovae: general – techniques: miscellaneous
Online Access:	https://eprints.nottingham.ac.uk/42324/

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