Improved Photometric Classification of Supernovae using Deep Learning
  We present improved photometric supernovae classification using deep
recurrent neural networks. The main improvements over previous work are (i) the
introduction of a time gate in the recurrent cell that uses the observational
time as an input; (ii) greatly increased data augmentation including time
translation, addition of Gaussian noise and early truncation of the lightcurve.
For post Supernovae Photometric Classification Challenge (SPCC) data, using a
training fraction of $5.2\%$ (1103 supernovae) of a representational dataset,
we obtain a type Ia vs. non type Ia classification accuracy of $93.2 \pm
0.1\%$, a Receiver Operating Characteristic curve AUC of $0.980 \pm 0.002$ and
a SPCC figure-of-merit of $F_1=0.57 \pm 0.01$. Using a representational dataset
of $50\%$ ($10660$ supernovae), we obtain a classification accuracy of $96.6
\pm 0.1\%$, an AUC of $0.995 \pm 0.001$ and $F_1=0.76 \pm 0.01$. We found the
non-representational training set of the SPCC resulted in a large degradation
in performance due to a lack of faint supernovae, but this can be migrated by
the introduction of only a small number ($\sim 100$) of faint training samples.
We also outline ways in which this could be achieved using unsupervised domain
adaptation.
