  One of the brightest objects in the universe, supernovae (SNe) are powerful
explosions marking the end of a star's lifetime. Supernova (SN) type is defined
by spectroscopic emission lines, but obtaining spectroscopy is often
logistically unfeasible. Thus, the ability to identify SNe by type using
time-series image data alone is crucial, especially in light of the increasing
breadth and depth of upcoming telescopes. We present a convolutional neural
network method for fast supernova time-series classification, with observed
brightness data smoothed in both the wavelength and time directions with
Gaussian process regression. We apply this method to full duration and
truncated SN time-series, to simulate retrospective as well as real-time
classification performance. Retrospective classification is used to
differentiate cosmologically useful Type Ia SNe from other SN types, and this
method achieves >99% accuracy on this task. We are also able to differentiate
between 6 SN types with 60% accuracy given only two nights of data and 98%
accuracy retrospectively.
